Functional Responses of Fish Communities to Environmental Gradients in the North Sea, Eastern English Channel, and Bay of Somme Matthew Mclean

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Functional responses of fish communities to environmental gradients in the North Sea, Eastern English Channel, and Bay of Somme Matthew Mclean

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Matthew Mclean. Functional responses of fish communities to environmental gradients in the North Sea, Eastern English Channel, and Bay of Somme. Ecology, environment. Université du Littoral Côte d’Opale, 2019. English. NNT : 2019DUNK0532. tel-02384271

HAL Id: tel-02384271 https://tel.archives-ouvertes.fr/tel-02384271 Submitted on 28 Nov 2019

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Functional responses of fish communities to environmental gradients in the North Sea, Eastern English Channel, and Bay of Somme

Réponses fonctionnelles des communautés de poissons aux gradients environnementaux en mer du Nord, Manche orientale, et baie de Somme

Présentée et soutenue par Matthew MCLEAN

Le 13 septembre 2019 devant le jury composé de :

Valeriano PARRAVICINI Maître de Conférences à l’EPHE Président Raul PRIMICERIO Professeur à l’Université de Tromsø Rapporteur

Camille ALBOUY Cadre de Recherche à l’IFREMER Examinateur Maud MOUCHET Maître de Conférences au CESCO Examinateur Rita P. VASCONCELOS Assistant Researcher à l’IPMA Examinateur Paul MARCHAL Directeur de Recherche à l’IFREMER Directeur de thèse Arnaud AUBER Cadre de Recherche à l’IFREMER Encadrant scientifique David MOUILLOT Professeur à l’Université de Montpellier Encadrant scientifique

Abstract

The ensemble of biological, geochemical, and physical processes that occur within ecosystems is driven by the interplay between biological communities and the abiotic environment. Explaining the spatial and temporal dynamics of biological communities in relation to environmental conditions is therefore essential for understanding ecosystem functioning, and ultimately for achieving sustainable development. In marine ecosystems, fish communities are key to ecosystem functioning, and fisheries provide livelihoods for over 10% of the world’s population. However, understanding the processes structuring fish communities remains difficult because community structure varies with both natural environmental fluctuations and, increasingly, human pressures. Effectively managing fisheries and marine ecosystems under global change therefore requires better characterizing fish community dynamics over time and space and disentangling the underlying drivers and mechanisms.

While fish ecologists have traditionally relied on species-based approaches (i.e., taxonomic approaches) to study community structure, trait-based approaches (i.e., functional approaches) are increasingly used because they can provide better insight into community assembly and the mechanisms driving community responses. To meet this need for a better understanding of biodiversity dynamics, the present thesis took advantage of long-term scientific monitoring data to characterize the functional responses of fish communities to environmental gradients in the North Sea, Eastern English Channel, and Bay of Somme. All three ecosystems experienced temperature rises and oceanographic changes associated with a warming phase of the Atlantic Multidecadal Oscillation (AMO), which rapidly impacted fish community structure. Consistent biological responses were observed across the three ecosystems despite their different spatial scales, demonstrating that fish communities were affected by environmental change through bio-ecological traits associated with habitat preference and life history. In the North Sea and Eastern Channel, pelagic species were the most responsive and

3 contributed largely to community dynamics, which is likely explained by their greater mobility, higher dispersal rates, and fewer habitat requirements. However, beyond habitat preference, species with r-selected life histories (e.g., low size and age at maturity, low parental investment, small offspring) had the fastest environmental responses whether or not they were pelagic, likely due to their rapid population turnover and generation time.

Importantly, the way these species’ responses shaped community structure depended on environmental context. R-selected, pelagic species rapidly declined in the Bay of Somme and

Eastern Channel, but rapidly increased in the North Sea. This likely reflects environmental suitability, indicating that after the phase change of the AMO, the Eastern Channel became a less favorable environment for these species, while the North Sea became more favorable.

Thus, species with high mobility and fast life history cycles appear capable of rapidly tracking environmental conditions, shifting in abundance in response to environmental suitability.

Additionally, as these ecosystems have warmed over the last 30 years, community responses were characterized by increases in mean thermal preference. Importantly, the amplitude of community changes was partially determined by communities’ initial structure and redundancy of bio-ecological traits, showing that community responses depended not only on environmental changes but also on biodiversity itself. Lastly, while fish community responses were consistently associated with climatic changes, historical fishing pressure on large- bodied, demersal species appeared to render fish communities more sensitive to environmental changes by increasing the relative of abundance of pelagic and r-selected species. Altogether this thesis provides important insight for anticipating changes in community structure and ecosystem functioning and suggests that global warming will favor species more adapted to warm conditions and species with rapid life history cycles will be the first to track environmental changes.

Keywords: bioecological traits, climate change, fish communities, fishing, functional ecology, resource management

Résumé en français

Il est essentiel de décrire la dynamique spatiale et temporelle des communautés de poissons pour comprendre le fonctionnement et les services des écosystèmes marins. Cette thèse a utilisé des données de surveillance scientifique à long terme pour caractériser les réponses fonctionnelles des communautés de poissons aux gradients environnementaux en baie de

Somme, Manche orientale et mer du Nord. Les trois écosystèmes ont connu des hausses de température associées à une phase de réchauffement de l'oscillation multidécennale de l'Atlantique, ce qui a eu des impacts rapides sur la structure des communautés. Dans les trois

écosystèmes, les réponses des communautés ont été médiées par des traits associés à l’habitat et à l’histoire de vie. Dans la mer du Nord et la Manche Est, les espèces pélagiques étaient les plus sensibles, ce qui s'explique probablement par leur mobilité et leur dispersion plus élevés.

Cependant, au-delà de l'habitat, les espèces à stratégie démographique r (faible taille et âge à maturité, faible investissement parental, etc.) ont eu des réponses environnementales plus rapides. La pression historique de la pêche semble d’avoir rendu les communautés plus sensibles aux changements environnementaux en augmentant l'abondance relative des espèces pélagiques et à stratégie r. De plus, comme ces écosystèmes se sont réchauffés au cours des

30 dernières années, les réponses des communautés ont été caractérisées par une augmentation de la préférence thermique moyenne, ce qui suggère que le réchauffement climatique favorisera des espèces mieux adaptées aux conditions chaudes et que les espèces ayant des cycles de vie rapides seront les premières à répondre.

Mots-clés: traits bioécologiques, changement climatique, communautés de poissons, pêche,

écologie fonctionnelle, gestion des ressources

Résumé étendu en français

Les océans fournissent de nombreux services écosystémiques, allant des ressources alimentaires et activités récréatives jusqu’aux traditions culturelles en passant par la régulation du climat. Ils représentent globalement plus de 60 % des services écosystémiques monétarisés (Barbier, 2017; Costanza et al., 1997). Parmi ces services, l'humanité exploite les ressources marines comme le poisson, mollusques et crustacés, pour la sécurité alimentaire et

économique de millions de personnes (Holmlund et Hammer, 1999; FAO, 2018; Worm et al.,

2006). À titre d’exemple, les océans fournissent près de 17 % des protéines consommées par l'homme dans le monde et la pêche mondiale génère environ 85 milliards de dollars par an

(FAO, 2018; Sumaila et al., 2011). L'Organisation des Nations Unies pour l'alimentation et l'agriculture (FAO) a signalé en 2016 que la consommation mondiale en poissons par habitant a doublé depuis les années 1960 et que le commerce mondial du poisson a augmenté de 300

% depuis cette date. Cette augmentation devrait s’amplifier avec une population humaine devant atteindre 9,7 milliards de personnes en 2050 (FAO, 2018). Notre dépendance croissante à l'égard des services écosystémiques issus de l’océan, en particulier les ressources halieutiques, souligne la nécessité de mieux comprendre le fonctionnement des écosystèmes marins pour optimiser nos stratégies de gestion des ressources et leur utilisation durable

(Barbier, 2017).

Actuellement, les écosystèmes marins sont en effet confrontés à de graves menaces aussi bien à échelle locale que globale. Au cours des 50 dernières années, la combinaison entre la surexploitation, le changement climatique, l’utilisation des terres et la pollution a eu des effets majeurs sur les écosystèmes marins, avec une augmentation constante de la proportion d'espèces dont le stock est considéré effondré ou quasiment éteint localement

(Worm et al., 2006). La pêche a ainsi entraîné la réorganisation des écosystèmes dans le

9 monde entier, avec 31 % des stocks mondiaux de poissons actuellement considérés comme surexploités, tandis que 58 % sont pleinement exploités. Toutefois, depuis les années 1990, les débarquements mondiaux ont progressivement diminué (Jackson et al., 2001; FAO, 2018).

L’autre grande menace qui pèse aujourd'hui sur les écosystèmes marins est vraisemblablement celle du changement climatique, car les activités humaines entrainent notamment des émissions croissantes de gaz à effet de serre qui perturbent les régimes climatiques et météorologiques sur Terre (IPCC, 2014). En effet, le dioxyde de carbone atmosphérique a récemment dépassé des niveaux sans précédent au cours des trois derniers millions d'années (Kahn, 2017). Jusqu'à présent, la température de la surface de la mer a augmenté de 0,7 °C au cours des 100 dernières années, et continuera d’augmenter jusqu’à 2,6

°C d'ici 2100 (IPCC, 2014; Simpson et al., 2011). De plus, les impacts de la pêche et du changement climatique peuvent agir en synergie et leurs effets combinés pourraient avoir des conséquences inconnues sur les écosystèmes marins.

Afin de comprendre les impacts des pressions naturelles et humaines sur la structure et le fonctionnement des écosystèmes, il est nécessaire d'étudier les réponses des communautés d’espèces aux gradients environnementaux. Étant donné l'influence majeure des interactions interspécifiques sur le fonctionnement des écosystèmes, l’étude des communautés offre de multiples avantages pour comprendre le fonctionnement des écosystèmes et, par conséquent, atteindre nos objectifs de développement durable. L'écologie des communautés examine la dynamique d'un ensemble d'espèces et relie cette dynamique à la variabilité environnementale, aux pressions humains, et aux interactions entre espèces (Gotelli, 2001).

Une telle approche peut ainsi donner un aperçu des mécanismes qui structurent les différents compartiments d’une communauté et de la façon dont les fluctuations environnementales, humaines et biologiques, peuvent influer sur les écosystèmes (Suding et al., 2008; Williams et al., 2010). L'étude de la dynamique des communautés de poissons est particulièrement

10 importante pour comprendre le fonctionnement des écosystèmes marins, car les poissons influencent les processus écosystémiques comme le stockage du carbone, le cycle des nutriments, la qualité de l'eau et la stabilité des habitats (Holmlund et Hammer, 1999; Villéger et al., 2017).

La dynamique spatiale et temporelle des communautés de poissons est fortement influencée par les conditions environnementales naturelles et les forces anthropiques

(Beukhof et al., 2019; Buchheister et al., 2013; Pecuchet et al., 2016). Ainsi, l'examen des influences relatives des facteurs naturels et anthropiques sur les communautés de poissons peut fournir des renseignements supplémentaires sur les fonctions des écosystèmes et leurs potentiels futures (Auber et al., 2015; Dulvy et al, 2008; Engelhard et al, 2011; McLean et al,

2016). Par conséquent, l'étude de la dynamique historique des communautés de poissons peut nous aider à comprendre comment les communautés répondent aux différentes pressions et à prédire les changements dans le fonctionnement des écosystèmes et les services associés, ce qui sera essentiel pour mieux planifier les efforts de conservation et de gestion.

Alors que les écologistes ont traditionnellement utilisé des approches basées sur les espèces (i.e., approche taxonomique) pour évaluer la dynamique des communautés et les réponses aux perturbations environnementales, l’approche fonctionnelle permet de mieux comprendre la dynamique et la structuration des communautés (Cadotte et al, 2011; Kiørboe et al, 2018; Villéger et al, 2017; Winemiller et al., 2015). Cette approche utilise les traits bioécologiques définis comme toute caractéristique morphologique, physiologique ou phénologique qui influe sur la croissance, la reproduction et la survie des espèces (Violle et al., 2007). En d’autres termes, l’approche fonctionnelle offre plusieurs avantages, en particulier celui de comprendre les filtres environnementaux et les mécanismes qui sous- tendent la dynamique des communautés (Dı́az et Cabido, 2001; Madin et al, 2016; Mcgill et al, 2006; Mouillot et al, 2013b).

Bien que les efforts de suivi des pêches soient importants à l'échelle mondiale (Pauly,

2007), il demeure difficile de déterminer les mécanismes sous-jacents qui structurent les communautés de poissons, en raison de leur variabilité sous les effets conjoints des changements environnementaux naturels et des perturbations humaines (Anderson et al.,

2008; Hsieh et al., 2006; Lehodey et al., 2006). De plus, malgré des décennies de collecte de données sur l'abondance et la répartition des poissons dans de nombreuses régions du monde, les réponses fonctionnelles des communautés aux changements environnementaux et aux pressions humaines ont été relativement peu étudiées (Dulvy et al., 2008; Micheli et Halpern,

2005; Somerfield et al., 2008). En effet, bien que les données issues des nombreuses campagnes de surveillance soient largement analysées pour évaluer les stocks commerciaux, elles ont été très peu exploitées en vue d’évaluer la diversité fonctionnelle des assemblages d’espèces. Pourtant, ces données massives offrent de nombreuses opportunités d'examiner la dynamique spatio-temporelle de la structure fonctionnelle des poissons en relation avec les changements environnementaux. Les programmes de surveillance scientifique à long terme permettent d’examiner la structure fonctionnelle des poissons en vue de mieux comprendre comment les communautés de poissons répondent aux changements environnementaux dans l'espace et le temps. En réponse à ce besoin, l'objectif principal de cette thèse a donc été d'examiner la dynamique à long terme des communautés de poissons dans trois écosystèmes interconnectés avec des échelles spatiales, des conditions environnementales et des niveaux de biodiversité différents afin d'identifier des relations entre les caractéristiques bioécologiques des espèces et leur environnement puis de dévoiler les mécanismes structurant les réponses des communautés de poissons aux variations environnementales. Ces trois

écosystèmes – la Mer du Nord, la Manche orientale et la Baie de Somme – offrent ainsi une excellente occasion d'examiner la dynamique spatiale et temporelle des communautés de

12 poissons, car ils ont fait l'objet de campagnes de surveillance scientifique annuelles au cours des trois dernières décennies.

Avant le début de cette thèse, Auber et al. (2015) ont révélé un changement majeur dans la structure des communautés de poissons de la Manche orientale. Leur étude a été réalisée via une approche taxonomique, évaluant les changements temporels dans les communautés et identifiant les espèces ayant le plus contribué à ces changements. Ces auteurs ont aussi constaté que le changement principal était une diminution marquée et rapide de l'abondance globale des poissons mais que les espèces les plus abondantes comme Trachurus trachurus, Trisopterus minutus, Sprattus sprattus et Trisopterus luscus avaient connu des baisses importantes alors que quelques espèces rares comme Scylorhinus stellaris, Mustelus asterias, Diacenturs labrax et Raja brachyura avaient légèrement augmenté en abondance.

L'objectif principal de cette thèse étant d'évaluer la dynamique des communautés de poissons par une approche fonctionnelle, les résultats d'Auber et al. (2015) ont constitué un excellent point de départ pour ce travail. J'ai donc commencé ma thèse en réexaminant le changement dans les communautés de poissons de la Manche orientale d'un point de vue des traits afin d'identifier les similitudes fonctionnelles entre les espèces dont l'abondance a subi des changements majeurs, de caractériser les changements globaux dans la structure fonctionnelle des poissons et d'identifier les associations entre les traits des poissons et les facteurs environnementaux. Ainsi au chapitre 2, j’ai utilisé une approche basée sur les traits combinée

à une nouvelle application statistique dite ‘Courbe de réponse principale’ pour caractériser le changement fonctionnel au sein des communautés de poissons de la Manche orientale observé

à la fin des années 1990. Les groupes fonctionnels ayant le plus fortement contribué au changement global de la structure fonctionnelle ont été identifiés et des relations significatives entre les traits et l'environnement ont été décelées. Les espèces pélagiques à cycle de vie rapide, à maturité sexuelle précoce, à faible niveau trophique, produisant des œufs de petite

13 taille, et accordant peu d’investissement parental (espèces à stratégie démographique « r ») ont considérablement diminué en abondance à la suite d'une augmentation de la température, ce réchauffement étant lié au passage d’une phase froide à une phase chaude d’un cycle naturel du climat appelé AMO (Atlantic Multidecadal Oscillation). L’influence de cette hausse des températures a vraisemblablement été amplifiée par la forte pression de pêche ayant précédé la période du réchauffement dans cette région. En revanche, les espèces à maturité sexuelle tardive, accordant davantage de soins parentaux à leurs descendants et produisant peu d’œufs (espèces à stratégie démographique « K ») ont augmenté en abondance, ce qui renforce l’hypothèse que les réponses des communautés au réchauffement climatique sont fortement influencées par les cycles de vie de chaque espèce. Ainsi, la mise en relation des facteurs environnementaux avec les traits fonctionnels nous a permis de mieux comprendre les mécanismes sous-jacents expliquant le changement de la structure des communautés de poissons en Manche orientale à la fin des années 1990.

Le chapitre 2 a permis de montré un changement majeur dans la structure fonctionnelle des communautés de poissons sur l'ensemble de la Manche orientale. Cette

étude a en particulier mis en évidence que le changement était similaire quel que soit le site d’étude en Manche orientale, mais que l'ampleur du changement était très variable d’un site à l’autre. Alors que certains sites présentaient des changements prononcés dans la structure fonctionnelle, d'autres étaient plus stables, sans relation visible avec la profondeur ou la distance de la côte. Cette constatation nous a incité à examiner si les changements observés pouvaient être liés à la nature même des communautés, notamment à leur composition initiale

(c’est à dire avant le changement communautaire) en traits fonctionnels comme la redondance fonctionnelle donc la présence de plusieurs espèces avec les mêmes traits. Les résultats ont montré que la structure fonctionnelle était responsable de l'ampleur du changement dans les communautés de poissons, en l’occurrence que la prédominance de certains traits fonctionnels

14 rendait les communautés plus sensibles aux changements environnementaux. De même, il a

été montré dans ce chapitre que les communautés ayant une redondance fonctionnelle plus

élevée étaient celles ayant été les plus résilientes au cours du temps, suggérant ainsi une contribution positive de la redondance fonctionnelle sur la stabilité des communautés soumises aux variations environnementales. Afin d’examiner la robustesse et la généralité de ces résultats, les données issues d'un écosystème tropical (îles des Seychelles) ont été utilisées. À nouveau, la sensibilité des communautés de poissons aux perturbations (ici blanchiment du corail) semble avoir été fortement dépendante de la structure et de la redondance fonctionnelle des communautés avant la perturbation. Dans les deux écosystèmes, il a été constaté que la prédominance des traits vulnérables aux variations climatiques rendait les communautés de poissons plus sensibles aux changements environnementaux, tandis que les communautés dont la redondance fonctionnelle était élevée étaient celles ayant le moins varié suite à l’épisode de blanchiment au milieu des années 1990. Cette étude est a priori l’une des premières à démontrer l'influence de la structure et de la redondance fonctionnelle des communautés sur leur sensibilité aux variations environnementales à grande échelle temporelle et spatiale. En résumé, ces résultats illustrent un lien cohérent entre la structure biologique et la sensibilité des communautés qui peut être transférable d'un écosystème à un autre et d'un taxon à l'autre, ce qui pourrait être bénéfique en vue d’anticiper les répercussions futures des perturbations naturelles et anthropiques sur la biodiversité et le fonctionnement des écosystèmes.

Les chapitres 2 et 3 ont documenté un changement majeur de la structure fonctionnelle des poissons dans la Manche orientale caractérisé par une diminution des espèces pélagiques et des espèces à cycle de vie rapide. Ce changement de structure fonctionnelle étant vraisemblablement lié à l’AMO, une nouvelle question s'est posée : a-t-on observé des changements similaires dans la structure fonctionnelle des écosystèmes estuariens de la

Manche orientale servant de zone de nourricerie pour de nombreuses espèces de poissons ? Si tel est le cas, non seulement cela indiquerait un lien important dans la dynamique fonctionnelle entre la Manche orientale et ses nourriceries, mais cela révélerait également des implications pour les communautés de poissons de la Manche orientale puisque les nourriceries sont d’une importance majeure pour le maintien des populations de poissons dans les écosystèmes adjacents de plus grande taille et exploités. Une dynamique fonctionnelle similaire entre la Manche orientale et ses nourriceries renforcerait aussi les conclusions sur les réponses des communautés de poissons aux variations climatiques à grande échelle. Afin de tester cette hypothèse, le chapitre 4 consistait à analyser une série historique de données d’abondances issues d’une campagne de suivi halieutique menée en baie de Somme. Cet estuaire constitue l’une des principales zones de nourricerie en Manche après la Baie de

Seine. Ainsi, le chapitre 4 porte sur l’étude des impacts des facteurs environnementaux et humains sur la dynamique spatiale et temporelle de la structure fonctionnelle des poissons pendant une phase de réchauffement de l’AMO. Il a été constaté que la nourricerie était au départ dominée par des poissons ayant une stratégie démographique r, autrement dit des espèces dont la maturité sexuelle est précoce, à faible niveau trophique, produisant des œufs de petite taille, et accordant peu ou pas de soins parentaux, et qui sont très sensibles aux changements environnementaux. L'effet de l’AMO, s’ajoutant au réchauffement planétaire d’origine anthropique, a provoqué un réchauffement rapide à la fin des années 1990 (plus de 1

°C de 1998 à 2003), ce qui a vraisemblablement entraîné une réorganisation fonctionnelle des communautés de poissons, avec une diminution d'environ 80 % de l'abondance globale des poissons et une augmentation de la dominance des espèces à stratégie démographique K. De plus, la pêche a encore probablement accentué la vulnérabilité de la baie aux changements climatiques en rendant les communautés davantage dominées par des espèces vulnérables aux variations climatiques. Enfin, en raison du lien entre la nourricerie et la Manche orientale, la

16 baisse des abondances en baie de Somme a vraisemblablement contribué à l’absence de restauration des stocks de poissons en Manche orientale après le changement de régime survenu à la fin des années 1990.

Pour la Manche orientale et la baie de Somme, les chapitres 2, 3 et 4 ont documenté d'importantes diminutions d'abondance pour les espèces pélagiques, à forte mobilité, à stratégie démographique r, ces dernières étant connues pour présenter des réponses rapides et de grande amplitude à la suite des variations environnementales. Ces résultats ainsi que ceux des études antérieures d'Auber et al. (2017a, 2015) ont indiqué un déplacement potentiel des espèces vers le nord en réponse à l'évolution rapide des conditions environnementales. Par exemple, à mesure que la Manche orientale se réchauffait, les espèces sensibles ont pu se déplacer vers le nord à la recherche de conditions environnementales plus favorables, soit par migration active, par la dérive des œufs et des larves, soit par des différences latitudinales de la mortalité, du recrutement ou même de la survie. Si tel est le cas, il serait vraisemblable d’observer une dynamique opposée dans le sud de la Mer du Nord. Ainsi, le chapitre 5 a été dédié à la comparaison des tendances temporelles de la structure fonctionnelle des poissons entre la Manche orientale et le sud de la Mer du Nord. Ces deux écosystèmes ont subi un changement rapide de structure fonctionnelle et ce de façon synchronisée avec l’oscillation climatique AMO. À l'aide d'analyses statistiques de causalité fondées sur le décalage temporel entre les variables d’effet et de réponse, il a été constaté que le réchauffement rapide a vraisemblablement amené les espèces pélagiques à stratégie r à se déplacer de la Manche vers la Mer du Nord, entraînant une inversion fonctionnelle entre ces deux écosystèmes connectés.

De plus, un décalage temporel d’un an a été détecté entre l'AMO et le changement fonctionnel, ce qui indique une réponse très rapide des communautés de poissons. Ce changement a probablement eu des conséquences majeures sur le fonctionnement de la Mer du Nord au travers de modifications de la dynamique des réseaux trophiques, des cycles des

17 nutriments ou encore du couplage benthique-pélagique. Pour les décennies à venir, il est prévu que les oscillations climatiques et les phénomènes météorologiques extrêmes soient de plus en plus fréquents et intenses, ce qui pourrait déclencher des changements fonctionnels majeurs avec de potentiels impacts sur le fonctionnement des écosystèmes et leurs services.

Le chapitre 5 visait à mettre en évidence des changements temporels dans les communautés de poissons du sud de la Mer du Nord à l'échelle de l'écosystème. Cependant, dans le cadre des objectifs de cette thèse, plusieurs questions importantes sont restées sans réponse. En particulier, la dynamique temporelle des communautés de poissons du nord de la

Mer du Nord n'a pas été étudiée, pas plus que la structure spatiale de la structure fonctionnelle des communautés de poissons dans toute la Mer du Nord. Ainsi, le chapitre 6 a dans un premier temps permis d’examiner le niveau d’hétérogénéité spatiale de la structure fonctionnelle des communautés de poissons dans l'ensemble de la Mer du Nord, puis de mettre en relation cette distribution spatiale de la biodiversité avec celle de l’environnement.

Dans un second temps, l'évolution de la structure spatiale des communautés et de l’environnement a été décrite afin de mieux estimer le niveau d’influence de l’environnement sur la structuration des communautés au fil du temps. En parallèle une comparaison des tendances temporelles a été effectuée afin de déterminer si les changements taxonomiques et fonctionnels ont été cohérents et similaires en ampleur et en direction. Il a été constaté que la majeure partie de la variation de la structure taxonomique et des traits s'expliquait par un gradient spatial prononcé. Il existe en effet un gradient de diversité biologique et environnemental fort entre les zones peu profondes du sud et les eaux froides et profondes du nord. D’un point de vue taxonomique, les communautés du sud et du nord ont divergé, autrement dit étaient composées de communautés de plus en plus différentes au fil des années.

A l’inverse, sur l’aspect fonctionnel, les communautés ont convergé puisque le nord et le sud ont vu au fil des années leurs communautés devenir de plus en plus similaires du point de vue

18 de leurs traits. En effet, plus le temps passait plus les communautés se sont retrouvées composées d’espèces pélagiques, de petite taille, à croissance rapide, et à préférence thermique élevée. Bien que la structure taxonomique ait changé au fil du temps, sa distribution spatiale est restée relativement stable, alors que dans la structure des traits, la zone sud de la Mer du Nord s'est déplacée vers le nord et s'est étendue, conduisant à une homogénéisation entre le nord et le sud. Ainsi, nos résultats ont suggéré que les changements environnementaux globaux, notamment le réchauffement climatique, conduiront à une convergence vers des traits plus adaptés aux nouveaux environnements, quelle que soit la composition en espèces.

Les chapitres 2 à 6 de cette thèse ont porté sur la structure fonctionnelle des communautés de poissons en intégrant plusieurs traits bioécologiques liés au cycle de vie, à l'écologie trophique et aux préférences d'habitat. Bien que des modifications de la biodiversité aient été décrites dans les chapitres précédents et que le principal changement environnemental concerne le réchauffement des eaux, aucun de ces chapitres n’avait encore porté sur une caractérisation uniquement basée sur la préférence thermique des espèces. Dans la grande majorité des sites étudiés en Mer du Nord une augmentation de la préférence thermique moyenne des communautés a été observée en parallèle à la hausse de la température. Dans le chapitre 6, la préférence thermique a été identifiée comme l’un des traits les plus explicatifs aux changements temporels de la structure des communautés de poissons.

Cette constatation a déjà été faite sur de nombreux autres écosystèmes où l’indice de préférence thermique moyenne des communautés (PTC) a été utilisé dans le cadre d’études portant sur de multiples compartiments biotiques tels que les poissons, les oiseaux, les papillons et les plantes (Cheung et al., 2013; Devictor et al., 2012; Gottfried et al., 2012). La majorité des études ont fait état d'augmentations de l'indice PTC à de multiples échelles spatiales en réponse à la hausse des températures. Une étude en particulier a documenté des

19 augmentations presque homogènes de l’indice PTC dans les communautés de poissons débarquées par les pêcheries du monde entier (Cheung et al., 2013). Cependant, d’un point de vue quantitatif, l'indice PTC reste une moyenne pondérée par les abondances au sein d’une communauté et ne peut donc par conséquent distinguer si une hausse de l’indice est la conséquence d’une hausse des abondances des espèces à préférence thermique élevée ou d’une baisse des abondances des espèces à préférence thermique faible. Répondre à cette question a d'importantes implications pour la compréhension et la conservation de la biodiversité. Malgré cela, aucune étude n'a, à notre connaissance, tenté de décomposer le signal de hausse ou de baisse de l’indice PTC. Le chapitre 7 a ainsi examiné les changements temporels de la préférence thermique moyenne des communautés de plusieurs écosystèmes côtiers de l’hémisphère nord tout en décomposant le signal de l’indice moyen. Il s’agissait dans un premier temps d'identifier quel était le processus sous-jacent à la variation observée de l’indice. Quatre processus étaient possibles : i) tropicalisation : augmentation de l'abondance des espèces préférant les eaux chaudes, ii) détropicalisation : diminution de l'abondance des espèces préférant les eaux chaudes iii) boréalisation : augmentation de l'abondance des espèces préférant les eaux froides, et iv) déboréalisation : diminution de l'abondance des espèces préférant les eaux froides. La distribution spatiale des 4 processus a d’abord été évaluée, puis mise en relation avec les caractéristiques de l’environnement. À ce niveau, il a été constaté que les changements à long terme de la préférence thermique moyenne des communautés de poissons sont fortement reliés à la température et ce avec un laps temps de réponse très rapide (0 et 1 an la plupart du temps). Cependant, l'indice PTC n'a augmenté que dans 58 % des endroits échantillonnés entre 1990 et 2015, et seulement 44 % d'entre eux étaient attribuables à une augmentation des espèces préférant les eaux chaudes

(tropicalisation) et 58 % à une diminution des espèces préférant les eaux froides

(déboréalisation). A l’inverse, les diminutions de l'indice PTC ont été principalement

20 attribuables à l'augmentation d’espèces préférant les eaux froides (63 %). Ces tendances n'étaient pas aléatoires dans l'espace, puisque l'indice PTC a surtout augmenté dans l'Atlantique, mais a diminué dans le Pacifique, suivant les changements récents de la température de surface de la mer. De plus, les changements de l'indice PTC ont été provoqués par des espèces préférant les eaux froides dans des endroits plus profonds, ce qui indique que la profondeur n’est pas forcément un refuge pour la réponse des communautés au réchauffement des océans.

Les conclusions issues de chaque chapitre appuient toutes le fait que les réponses des communautés de poissons aux gradients environnementaux sont opérées via les traits bioécologiques des espèces. La préférence de l'habitat semble le trait le plus important pour expliquer les réponses des communautés aux changements environnementaux, car les espèces pélagiques étaient beaucoup plus sensibles que les espèces démersales, probablement en raison d'une plus grande mobilité, d'une plus grande capacité de dispersion et de moindres exigences en termes de caractéristiques d’habitat (Montero-Serra et al., 2014). En Mer du

Nord (et dans d'autres grands écosystèmes marins), la préférence thermique a également largement contribué aux réponses des communautés, puisque la préférence thermique moyenne des communautés a suivi de près les changements de la température de l’eau. De plus, conformément à la théorie et aux résultats découverts pour d'autres organismes, cette thèse a révélé que les réponses des communautés aux changements environnementaux s'expliquaient en grande partie par les stratégies démographiques r et K (Devictor et al., 2012;

King et McFarlane, 2003; Mims et Olden, 2012). Le chapitre 5 a démontré que bien que les espèces pélagiques étaient les plus liées aux changements entre la Manche orientale et le sud de la Mer du Nord, les espèces à stratégie démographique r avaient des réponses environnementales plus importantes que les espèces à stratégie K, qu'elles soient pélagiques ou démersals. Dans l'ensemble, il semble donc que le trait le plus important dans la réponse

21 des communautés de poissons aux changements environnementaux est l'utilisation de l'habitat, les poissons pélagiques ayant les réponses environnementales les plus marquées et les plus rapides.

Dans ce travail de thèse, les facteurs de forçage qui ont été sélectionnés et pouvant induire des changements temporels dans la structure fonctionnelle des communautés de poissons étaient le climat et la pêche. Bien qu'une augmentation de la température de la surface de la mer associée à une phase de réchauffement de l'AMO ait été identifiée comme le principal moteur de la dynamique des communautés de poissons dans les trois écosystèmes, les effets de la pêche ont aussi été mis en évidence. Au début des années 1990, la Manche orientale était fortement dominée par de petites espèces à cycle de vie rapide, en particulier les petits pélagiques (Auber et al., 2015). Ce type de structuration est la conséquence d’une pêche historiquement intense dans cette zone qui a principalement visé les espèces démersales de plus grande taille et au cycle de vie moins rapide (Molfese et al., 2014; Pauly et al., 1998).

Après cette surpêche, les communautés étaient dominées par des espèces à stratégie r et donc plus sensibles aux variations environnementales, ces dernières ont alors présenté des réponses rapides et marquées, ce qui montre à quel point la pêche peut rendre les communautés de poissons plus vulnérables au climat (Hsieh et al., 2006). De plus, en Mer du Nord, la surpêche de grandes espèces a indirectement diminué la pression de prédation sur les espèces de plus petite taille à cycle de vie rapide, ce qui a probablement contribué à leur augmentation (Daan et al., 2005). L'élévation de la température associée à l'AMO semble ainsi avoir favorisé les espèces à stratégie r et, compte tenu de leur caractère opportuniste, ces espèces ont pu augmenter rapidement dans l'ensemble de l'écosystème. Bien qu'aucun impact direct de la pêche n'ait été observé dans la baie de Somme, les espèces dont l'abondance a augmenté, notamment le bar (Dicentrarchus labrax) et le turbot (Scophthalmus maximus), ont été moins exploitées dans la Manche orientale ces dernières décennies, ce qui pourrait avoir facilité leur

22 augmentation dans la baie. Ainsi, les impacts de la pêche en Manche orientale semblent avoir eu des conséquences à l’échelle très locale dans la baie de Somme. Toutefois, au regard de l’ensemble des résultats obtenus durant ce travail de thèse, les variations climatiques semblent avoir été la principale cause des changements structurels au sein des communautés de poissons (Henson et al., 2017), notamment en raison de la forte synchronicité entre le climat et la réponse des communautés et par le fait que ce sont des traits bioécologiques connus pour

être sensibles au climat qui ont présenté des patrons temporels particulièrement distincts (ex. via l’indice PTC).

Au final, les résultats de cette thèse appuient fortement l’utilisation d'approches fondées sur les traits bioécologiques pour décrire et comprendre les réponses des communautés aux gradients environnementaux. En découvrant les relations trait- environnement et en identifiant les traits écologiques regroupant les espèces sensibles, cette thèse a permis d'obtenir une meilleure compréhension des mécanismes de réponse des communautés de poissons aux gradients environnementaux, ce qui n'était pas envisageable avec une approche purement taxonomique. Le chapitre 2 a montré qu’un grand nombre de sites ont connu des changements notables dans la structure des communautés lorsque celles-ci

étaient caractérisées par les traits alors que l’approche taxonomique ne permettait pas de détecter de tels changements (Auber et al., 2017b). Le chapitre 6 a démontré qu'il était possible de trouver une dynamique contrastée entre la structure fonctionnelle et taxonomique des communautés. Bien que des études antérieures aient déjà examiné les changements dans la structure taxonomique des communautés de ces écosystèmes (Auber et al., 2017a, 2017b,

2015; Beare et al., 2004), une approche fondée sur les traits était nécessaire pour élucider les mécanismes sous-jacents à la structuration des communautés et pour déterminer les caractéristiques écologiques qui ont déterminé la manière dont les espèces ont réagi aux changements environnementaux.

Les chapitres 2 et 5 ont documenté des changements majeurs de la structure en taille et de la dominance des poissons pélagiques en Manche orientale et Mer du Nord. Ces chapitres ont aussi tenté d’identifier des conséquences potentielles des changements observés sur le fonctionnement des écosystèmes. En effet, de telles modifications de la biodiversité sont susceptibles d'avoir engendré des impacts importants sur les processus écologiques comme le couplage bentho-pélagique, la productivité, la séquestration du carbone, le renouvellement de la biomasse et la stabilité des écosystèmes (Baustian et al., 2014; Blanchard et al., 2011; Houk et al., 2017; Rooney et al., 2006). Des changements dans la structure fonctionnelle sont

également susceptibles de modifier les réseaux trophiques et la distribution de la biomasse entre les différents niveaux trophiques, ce qui pourrait avoir un impact sur les activités de pêche (Frank et al., 2005; Österblom et al., 2007). Le chapitre 3 a notamment montré que la sensibilité des communautés aux perturbations environnementales peut être déterminée par la structure fonctionnelle des communautés et la redondance, ce qui a des implications importantes pour la résistance et la résilience des écosystèmes. Cette constatation suggère que la stabilité de la communauté face aux perturbations environnementales peut être directement déterminée par la structure biologique et la redondance, et que les stratégies de gestion devraient donc viser à maximiser l'exploitation des ressources sans compromettre la structure et la redondance fonctionnelle des communautés. À titre d’exemple, les efforts de pêche pourraient être concentrés sur des rôles écologiques hautement redondants (D'agata et al.,

2016b). Le chapitre 4 a listé plusieurs implications importantes pour les pêches et le fonctionnement des écosystèmes, non seulement en baie de Somme, mais potentiellement pour des écosystèmes similaires de type nourricerie dans d'autres régions géographiques. La baie de Somme est une importante zone de nourricerie pour la Manche orientale, et l'effondrement majeur de l'abondance des poissons a probablement causé une rétroaction négative, empêchant le rétablissement des populations dans la Manche. Les estuaires sont des

24 habitats importants qui contribuent au maintien de grands écosystèmes marins à l'échelle mondiale et sont essentiels pour de nombreuses populations de poissons commerciaux. Il est donc urgent de poursuivre les suivis temporels des communautés de poissons dans d'autres nourriceries afin d’évaluer les risques d’effondrements et les échéances associées.

Le chapitre 6 a probablement fourni les résultats les plus importants pour les pêcheries et la gestion écosystémique. Ce chapitre a montré que les communautés de zones différentes sont devenues de plus en plus similaires du point de vue de leur structure fonctionnelle bien qu’elles devenaient de plus en plus dissimilaires du point de vue taxonomique. Cette convergence fonctionnelle était caractérisée par des traits davantage associés au réchauffement des eaux, ce qui suggère que les communautés de poissons du monde entier pourraient potentiellement s'homogénéiser vers des structures essentiellement composées d’espèces généralistes, à préférence thermique élevée, de petite taille, à croissance rapide et pélagiques. Le suivi des communautés par une approche purement taxonomique n'aurait pas permis de déceler un tel résultat. La gestion des stocks de poissons par l’approche taxonomique n’est donc pas optimale, en particulier dans le contexte actuel du changement climatique. La gestion écosystémique doit identifier les traits bioécologiques susceptibles d'augmenter dans les communautés de poissons et adapter les stratégies de gestion en prévision de ces changements. Enfin, le chapitre 7 offre des orientations importantes pour anticiper les changements futurs de la biodiversité dans le contexte du changement climatique.

S’il était possible de déterminer avec précision et certitude la façon dont le réchauffement climatique va modifier la structure thermique des communautés, il serait par exemple possible de prévoir comment évoluera la productivité des pêches dans les décennies à venir et ce dans diverses régions du monde.

L'écologie marine évoluant de plus en plus vers des approches fonctionnelles, les traits des espèces devraient aussi être de progressivement intégrés dans les processus de mise en

25 place de la gestion des pêches. Le plus grand avantage de l'approche fonctionnelle, dans un contexte de gestion, est la capacité d'appliquer les résultats à différents écosystèmes avec différents compositions d'espèces. Cette transférabilité des résultats pourrait constituer un lien majeur pour la gestion des ressources. Plutôt que de consacrer du temps et des ressources à l'identification de stratégies de gestion optimales pour chaque espèce et chaque écosystème, les scientifiques et les gestionnaires des ressources pourraient identifier des stratégies de gestion applicables sur différents types d’écosystèmes en se concentrant sur les traits et non sur les espèces. Ces approches pourraient par exemple considérer les groupes fonctionnels comme des unités de gestion ou même examiner comment différents scénarios de pêche impactent la structure fonctionnelle des communautés.

A l’issue de ce travail de thèse, la perspective la plus pertinente est l’incorporation de ces connaissances dans le processus de prédiction de la réponse de la biodiversité à divers scénarios de changement climatique et de pêche. Les modèles mécanistes et les modèles statistiques déjà élaborés pour prédire les changements dans la structure taxonomique des communautés devraient être combinés avec les traits pour prédire les changements futurs de la structure fonctionnelle des communautés, le fonctionnement des écosystèmes et potentiellement les services écosystémiques. Des modèles d'habitat pourraient également être

élaborés directement en fonction des relations trait-environnement. De tels modèles pourraient alors prédire directement les changements dans la structure des traits des communautés en réponse aux changements climatiques et aux stratégies de pêche. Étant donné que de nouvelles études établissent un lien entre les caractéristiques des espèces et les processus

écologiques, de tels modèles pourraient aussi permettre d'anticiper les changements attendus sur le fonctionnement des écosystèmes et par conséquent de tendre vers l’objectif de développement durable.

Acknowledgements

I would like to first express sincere gratitude to my thesis advisors – Arnaud Auber and David

Mouillot – whose generous support, encouragement, and guidance made my thesis both a success and a pleasure. Beyond the countless hours dedicated to discussing, reviewing, and editing my work, you both provided invaluable personal and moral support. You not only taught me immensely about all aspects of research, from data analysis and publishing to networking and career planning, you also welcomed me into your homes, provided many well-needed beers, and made me feel at home in a foreign country. I could not have had a better pair of advisors, and that is clearly reflected in the work we accomplished together.

Special thanks to Paul Marchal, my thesis director and laboratory supervisor, who not only provided me with the necessary resources, logistical support, and even French courses, but made extra efforts to ensure I was comfortable and well-assimilated in my new foreign setting. Paul, your endless support and exceptional personal character made it a pleasure working in your lab.

To all the researchers, postdocs, technicians, and students I’ve had the pleasure of meeting in my time at Ifremer (and Université de Montpellier) – thank you for making me feel welcome, for teaching me your language and culture, for showing me many great soirées, and for being a bright ray of sun in a not-so-sunny city.

I would like to thank my exceptional thesis committee, including not only Arnaud,

David, and Paul, but also Anik Brind’Amour, Georg Engelhard, Martin Lindegren, and

Sébastien Villéger. It was a pleasure collaborating and learning from you all, and I thank you for your continual willingness to answer questions, provide opinions, and critique my work.

Your input greatly improved not only the quality of this thesis but also my knowledge and abilities as a young researcher.

To my parents, I cannot thank you enough for all that you have provided me in life and for your never-ending support. Not many parents would be fully supportive of a 30-year-old student, but you have always encouraged me to seek passion in life, to chase my dreams, and to pursue what I love, and you have expressed your pride every step of the way.

Most of all I would like to thank my wife Mary Jo who helped me follow my dreams by joining me in this crazy adventure. Between living in the middle of the Pacific Ocean and learning a foreign language, you have truly done everything to support me not only in my career but in all aspects of life, and everything I have accomplished I owe to you.

Finally, I would like to sincerely thank Électricité de France, la Région Hauts-de-

France, la Fondation Pour la Recherche Sur la Biodiversité, and Ifremer who provided the funding that made this thesis possible.

Table of Contents

1. General introduction ...... 35 1.1. Biodiversity and ecosystem functioning ...... 36 1.1.1. Ecosystem functioning and services ...... 36 1.1.2. Influence of biodiversity on ecosystem functioning ...... 38 1.1.3. Threats to biodiversity and ecosystem services ...... 39 1.2. Sustainable development and ecosystem-based management ...... 42 1.3. Fish community dynamics ...... 43 1.4. Functional ecology ...... 44 1.5. Methodology ...... 46 1.5.1. Choosing bio-ecological traits ...... 46 1.5.2. Functional space ...... 48 1.5.3. Community weighted means ...... 50 1.6. Study ecosystems ...... 51 1.6.1. North Sea ...... 52 1.6.2. Eastern English Channel ...... 55 1.6.3. Bay of Somme ...... 57 1.7. Thesis objectives ...... 58 2. Ecological and life-history traits explain a climate-induced shift in a temperate marine fish community ...... 60 2.1. Preface ...... 61 2.2. Introduction ...... 62 2.3. Materials and methods ...... 64 2.4. Results ...... 71 2.5. Discussion ...... 76 2.6. Supplementary material ...... 82 3. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities ...... 85 3.1. Preface ...... 86 3.2. Introduction ...... 87 3.3. Materials and Methods ...... 89 3.4. Results ...... 101 3.5. Discussion ...... 107 3.6. Supplementary material ...... 114

4. Functional reorganization of marine fish nurseries under climate warming ...... 124 4.1. Preface ...... 125 4.2. Introduction ...... 126 4.3. Materials and methods ...... 128 4.4. Results ...... 137 4.5. Discussion ...... 145 4.6. Supplementary material ...... 154 5. A climate-driven functional inversion of connected marine ecosystems 157 5.1. Preface ...... 158 5.2. Introduction ...... 159 5.3. Methods and materials ...... 160 5.4. Results ...... 166 5.5. Discussion ...... 171 5.6. Supplementary material ...... 178 6. Fish communities diverge in species but converge in traits over three decades of warming ...... 183 6.1. Preface ...... 184 6.2. Introduction ...... 185 6.3. Methods ...... 187 6.4. Results ...... 194 6.5. Discussion ...... 203 6.6. Supplementary material ...... 210 7. Deborealization exceeds tropicalization in marine fish community thermal shifts ...... 217 7.1. Preface ...... 218 7.2. Introduction ...... 219 7.3. Materials and Methods ...... 221 7.4. Results ...... 227 7.5. Discussion ...... 230 7.6. Supplemental Material ...... 235 8. General discussion and perspectives ...... 242 8.1. Conclusions ...... 243 8.2. Implications for ecosystem functioning and management ...... 248 8.3. Limitations ...... 249 8.4. Future research directions ...... 252

9. References...... 257 10. Appendix A: Which traits are most responsive to environmental change: interspecific differences blur trait dynamics in classic statistical analyses ...... 286 11. Appendix B: Publications arising from this thesis ...... 304 12. Appendix C: Publications during candidature not arising from this thesis ...... 306

1. General introduction

1.1. Biodiversity and ecosystem functioning

1.1.1. Ecosystem functioning and services

‘Ecosystem functioning’ refers to the biological, geochemical, and physical processes that occur within ecosystems that determine ecosystem structure and stability (Hooper et al., 2005;

Loreau et al., 2001; Solan et al., 2012; Tilman et al., 2014). Ecosystems are complex networks of interactions between living organisms and the physical environment, and these interactions drive ecological processes like energy flow and nutrient cycling that determine food web structure and ecosystem productivity (Naeem et al., 1999; Palumbi et al., 2009; Worm et al.,

2006). Energy flow refers to the movement of organic material through ecosystems as sunlight is converted to chemical energy by photosynthesis (or when inorganic materials are converted to organic matter by chemosynthesis) and transferred throughout the food web via trophic interactions (Naeem et al., 1999; Schindler et al., 1997; Solan et al., 2012). As energy and materials move throughout the ecosystem, carbon and nutrients like nitrogen and phosphorous are stored and cycled among different ecosystem compartments (Rooney et al.,

2006; Taylor et al., 2006). Together the rates and dynamics of these energy and material fluxes determine ecosystem functionality and the goods and services that ecosystems provide to human societies (Brodie et al., 2018; Louca et al., 2016; Taylor et al., 2006). For example, in marine ecosystems, energy and materials are reciprocally transferred between pelagic and benthic compartments (i.e., benthic-pelagic coupling) as planktonic matter sinks into benthic energy channels, and nutrient re-suspension and larval production by benthic organisms are fed back into the pelagic food web (Baustian et al., 2014; Giraldo et al., 2017). Such energy coupling not only maintains overall ecosystem structure and stability but also enhances fisheries production available for human consumption (Blanchard et al., 2011; Griffiths et al.,

2017; Rooney et al., 2006).

The world’s oceans provide several ecosystem services including not only harvestable goods, but also climate regulation, recreation, and even cultural tradition, together accounting for over 60% of global ecosystem services financially (Barbier, 2017; Costanza et al., 1997).

The oceans maintain Earth’s climate and primary productivity by diffusing solar energy, gases, and nutrients along oceanic currents and serve as major carbon sinks through atmospheric mixing and biological assimilation (Schmitt, 2018). Photosynthesis by oceanic phytoplankton also accounts for over 50% of global oxygen production (Lin et al., 2003). On local scales, marine ecosystems such as reefs, mangroves, and wetlands provide coastal protection against storms and land erosion, valued at upwards of 2600 USD ha-1 annually

(Barbier et al., 2011; Rao et al., 2015; Spalding et al., 2014). Marine ecosystems also provide substantial recreational opportunities and generate major economic revenue through tourism

(Daily, 2003; Klein et al., 2004; Orams, 2002). However, mankind benefits most intimately from marine ecosystems through harvesting natural resources like fish and shellfish, which provides food and economic livelihoods for millions of people (Holmlund and Hammer,

1999; FAO, 2018; Worm et al., 2006). For instance, the oceans provide roughly 17% of human consumed protein worldwide and global fisheries generate roughly 85 billion USD per year (FAO, 2018; Sumaila et al., 2011). The Food and Agriculture Organization of the United

Nations (FAO) reported in 2016 that global per capita fish consumption has doubled since the

1960s and that global fish trade has increased by 300%, and human population is further expected to reach 9.7 billion people by 2050 (FAO, 2018). Our growing dependence on ocean ecosystem services, particularly natural resources like fisheries, highlights the need to understand marine ecosystem functioning to improve conservation and resource management

(Barbier, 2017).

1.1.2. Influence of biodiversity on ecosystem functioning

Biodiversity refers to the variety of life on Earth, and it can be measured at scales ranging from genes to biomes. At the community level, biodiversity can be further considered taxonomically (species diversity), functionally (trait diversity), or even phylogenetically

(evolutionary diversity), but at large scales is often measured through species richness. The oceans cover over 70% of the planet and marine ecosystems are characterized by a striking diversity of both environments and organisms. Globally, marine ecosystems host over

250,000 identified species with an estimated 750,000 yet to be described (Solan et al., 2012).

This diversity is crucial to marine ecosystem functioning as studies over the last three decades have consistently demonstrated positive relationships between biodiversity and ecosystem functioning across multiple taxa (Cardinale et al., 2006; Duffy et al., 2016; Isbell et al., 2015;

Soliveres et al., 2016). For example, a variety of experimental studies have found that diversity increases productivity and stability in plant and microbial communities (Cardinale et al., 2006; García et al., 2018; Tilman et al., 2001). In fish communities, higher diversity has been shown to increase biomass production and to buffer communities against environmental fluctuations (Duffy et al., 2016). Fisheries yields have also been shown to be less vulnerable to environmental fluctuations when catch diversity is higher (Dee et al., 2016).

Biodiversity may enhance ecosystem functioning through several mechanisms, notably niche complementarity, the selection effect, and the portfolio effect (or insurance hypothesis). Niche complementarity arises when a species pool performs more efficiently than the individual species themselves because competition and functional differences among species lead to greater overall resource use (Cadotte, 2017; García et al., 2018; Hooper et al.,

2005). For example, Burkepile and Hay (2008) showed that diverse fish assemblages consumed a greater proportion of available resources than any individual species alone due to resource partitioning and functional nuances between species. The selection effect refers to

38 increased ecosystem functioning due to a greater likelihood of including functionally- important species at higher overall diversity (Dı́az and Cabido, 2001; García et al., 2018;

Gravel et al., 2016a). For instance, García et al. (2018) found that higher diversity microbial communities were more likely to include species with high thermal tolerances, stabilizing communities against warming and maintaining ecosystem productivity. The portfolio effect, or insurance hypothesis, additionally states that ecosystem functioning and stability should increase with increasing diversity if species have asynchronous environmental responses, because stable species can compensate for unstable species (Oliver et al., 2015; Schindler et al., 2015; Thibaut and Connolly, 2013).

1.1.3. Threats to biodiversity and ecosystem services

Marine ecosystems are facing severe threats from local to global-scale stressors, with current species extinction rates widely surpassing those previously seen in fossil records (Ceballos et al., 2015). The combination of overexploitation, climate change, land development, and pollution has dramatically impacted marine ecosystems, with a steady increase in the proportion of collapsed or locally-extinct species over the last 50 years (Worm et al., 2006).

Historical overfishing has led to ecosystem collapses and reorganizations worldwide, and

31% of global fish stocks are currently overexploited, while 58% are fully exploited, and global landings have steadily declined since the mid-1990s (Jackson et al., 2001; FAO, 2018).

Fishing affects marine ecosystems both directly and indirectly. Large, long-lived species are highly targeted and are generally the first species directly removed by fisheries. Because large, long-lived species have low mortality rates and slow generation times they are highly vulnerable to fishing pressure and quickly decline following exploitation (Adams, 1980; Daan et al., 2005). Removing large-bodied species with high trophic position has a cascading effect for the overall ecosystem because smaller prey species are released from predation and can

39 subsequently increase in abundance and overexploit lower trophic levels like zooplankton

(Bascompte et al., 2005; Casini et al., 2009). Removing top predators also reduces overall ecosystem diversity because predation promotes species coexistence by reducing competitive exclusion (Paine, 1966; Rooney et al., 2006). Because fishing pressure often targets larger individuals, even within a single population, fishing can also disrupt demographic structure, leading to reduced size spectra and earlier sexual maturation. This has negative feedbacks for ecosystem resilience because fecundity increases exponentially with body size and age, and smaller, younger individuals produce less viable offspring (Birkeland and Dayton, 2005).

Disrupted demographic structure also increases population variability, which can reduce food web stability and increase susceptibility to environmental fluctuations (Hsieh et al., 2006).

Fishing may also disrupt ecosystem functioning when targeted species support key ecological processes. For example, when herbivorous fishes are removed from marine ecosystems, regime shifts in benthic community structure can rapidly occur due to prolific algal overgrowth (Bellwood et al., 2003; Mumby et al., 2006).

Despite the destructive impacts of overfishing, perhaps the greatest threat facing marine ecosystems today is climate change, as ongoing human activities, notably greenhouse gas emissions, are disrupting Earth’s natural climate and weather patterns (IPCC, 2014). The most immediate impact of climate change is global warming, which can profoundly affect biodiversity because physiological processes like metabolism are temperature-dependent, and many organisms have narrow thermal tolerances (Pörtner and Farrell, 2008). For instance, in exothermic organisms, increased temperatures lead to higher metabolic demands that can reduce organismal growth, reproduction, and survivorship (Pörtner and Knust, 2007). Global sea surfaces temperatures have so far risen by 0.7°C in the last 100 years, and further increases of up to 2.6°C are expected by 2100 (IPCC, 2014; Simpson et al., 2011). Marine ecosystems are already heavily impacted by global warming, with well-documented changes

40 in species distributions, particularly poleward distribution shifts, and increasing dominance by species with warm temperature preferences (Cheung et al., 2013; Day et al., 2018; Molinos et al., 2016; Sunday et al., 2015). For example, in the North Sea, temperatures have risen four times faster than the global average (Hobday and Pecl, 2014) and many species have shifted northward or to deeper waters to maintain optimal temperatures (Dulvy et al., 2008; Perry et al., 2005). In the Barents Sea, reduced sea ice cover enabled a northward shift of boreal species that reorganized fish community composition and likely disrupted benthic-pelagic coupling (Frainer et al., 2017). At the global scale, Cheung et al. (2013) reported an increase in the mean temperature preference of commercial fisheries catches worldwide that was linked to increasing sea surface temperatures. Beyond warming, climate change is also leading to ocean acidification, decreased dissolved oxygen, sea level rise, more frequent and intense storms, and even changes in ocean circulation, all of which could have profound impacts on marine biodiversity and ecosystem functioning (Beaugrand et al., 2015; Bellard et al., 2012; Brierley and Kingsford, 2009; Claret et al., 2018). For instance, higher acidity increases the energetic cost of osmoregulation, which can reduce organismal performance, and under extreme conditions acidification may hinder skeleton and otolith development

(Fabry et al., 2008). Reduced oxygen content also imposes an additional negative feedback because warmer temperatures increase organisms’ oxygen demands (Pörtner and Farrell,

2008). Disruption of oceanic currents would have major impacts on marine ecosystem structure, as hydrological conditions could change entirely. For instance, disruption of the

Atlantic Gulf Stream, which transports warm sub-tropical waters northwest across the

Atlantic Ocean, would drastically decrease temperatures along the European coast, leading to a completely novel ecosystem configuration (Thornalley et al., 2018).

1.2. Sustainable development and ecosystem-based management

Sustainable development refers to using natural resources to advance human society without compromising ecosystems’ abilities to sustain themselves or to continue providing ecosystem services into the future (Chichilnisky, 1997; Robert et al., 2005). The concept of sustainable development emerged as overexploitation and ecosystem collapse demonstrated the need to improve resource management to meet human development needs in the long term

(Chichilnisky, 1997; Du Pisani, 2006). For example, overexploitation of cod stocks in

Newfoundland caused a major collapse in the late 1990s that led to an indefinite fisheries closure (Milich, 1999). Such examples show the need to devise sustainable management strategies that balance socio-economic gain with long-term environmental durability. Yet, despite decades of fisheries research and monitoring, over 30% of global fish stocks remain overexploited, highlighting the need to improve our understanding of biodiversity and ecosystem functioning (FAO, 2018). Historically, fisheries management operated on a stock- by-stock basis, but has progressively moved toward an ecosystem-based approach following the recognition of the ecosystem effects of overfishing (Lubchenco and Levin, 2008; Pikitch et al., 2004). Ecosystem-based management is a holistic approach that integrates not only individual species, but also community dynamics, environmental changes, and human impacts

(Lubchenco and Levin, 2008; Pikitch et al., 2004). Ecosystem-based management must therefore consider species’ life histories, interactions, trophic positions, habitat requirements, and responses to environmental variation, which ultimately requires an intimate understanding of community dynamics.

1.3. Fish community dynamics

Given the considerable influence of inter-specific interactions in ecosystem functioning, examining biodiversity at the community-scale provides multiple advantages for understanding ecosystem functioning and therefore achieving sustainable development.

Community ecology examines the dynamics of an array of species simultaneously and links these dynamics to environmental variability, food resources, and species interactions (Gotelli,

2001). Disentangling such patterns can provide insight into the mechanisms structuring different ecosystem compartments and how environmental and biological fluctuations may impact ecosystems (Suding et al., 2008; Williams et al., 2010). Studying fish community dynamics is particularly important for understanding marine ecosystem functioning as fishes influence ecosystem processes like carbon storage, nutrient cycling, water quality, and habitat maintenance (Holmlund and Hammer, 1999; Villéger et al., 2017). Moreover, fish communities influence ecosystem stability because changes in fish communities can generate cascading effects for overall ecosystem structure (Bascompte et al., 2005). For example, fluctuations in cod abundances in the Baltic Sea have induced trophic cascades in zooplankton composition that can shift the ecosystem between oligotrophic and eutrophic states (Casini et al., 2009; Österblom et al., 2007).

The spatial and temporal dynamics of fish communities are strongly driven by both natural environmental conditions and anthropogenic forces (Buchheister et al., 2013; Daan et al., 2005; Jennings et al., 1999b; Jung and Houde, 2003). As marine ecosystems are increasingly impacted by anthropogenic forces like overfishing and climate change, understanding the effects on community structure and function will be essential for achieving sustainable development (Hoegh-Guldberg and Bruno, 2010; Jackson et al., 2001; Jeppesen et al., 2010; Pauly et al., 2002; Perry et al., 2005; Worm et al., 2006). Examining the relative influences of environmental drivers (both natural and anthropogenic) on fish communities can

43 further provide insight into changes in biodiversity and ecosystem function, and how ecosystem function might change in the future (Auber et al., 2015; Dulvy et al., 2008;

Engelhard et al., 2011; McLean et al., 2016). Therefore, investigating the historical dynamics of fish communities may help us understand how communities respond to environmental pressures and to anticipate changes in ecosystem functioning and ecosystem services, which will be critical for planning conservation and management efforts.

1.4. Functional ecology

Ecologists have traditionally used species-based approaches to assess community dynamics and responses to environmental disturbances; yet, a growing body of research shows that species-based approaches may be insufficient for understanding the mechanisms structuring communities and for predicting ecosystem processes (Cadotte et al., 2011; Carmona et al.,

2016; Darling et al., 2012; Hooper et al., 2005; Kiørboe et al., 2018; Villéger et al., 2017;

Walker, 1992; Winemiller et al., 2015). In contrast to species-based approaches, the functional approach uses bio-ecological traits defined as any morphological, physiological or phenological feature that impacts fitness via its effects on growth, reproduction and survival

(Violle et al., 2007). Bio-ecological traits can be further classified as response and effect traits, where response traits are those that influence how organisms respond to environmental changes, while effect traits are believed to have direct influences on ecosystem processes

(Figure 1) (de Bello et al., 2010; Pakeman, 2011; Pillar et al., 2013). Trait-based approaches offer several advantages, particularly for understanding environmental filtering and the mechanisms driving community dynamics (Dı́az and Cabido, 2001; Madin et al., 2016; Mcgill et al., 2006; Mouillot et al., 2013b). For example, examining community changes from a trait- based perspective can identify the characteristics linking species with similar environmental

44 responses and can therefore identify why different species show strong or weak responses.

Identifying the most responsive traits and linking them to environmental drivers can therefore help generate predictive relationships about how environmental changes could affect community and ecosystem structure (Mouillot et al., 2013b). Additionally, the results and conclusions of trait-based studies are transferable across ecosystems, which could help anticipate ecosystem changes in entirely different geographic regions.

Figure 1 | Conceptual figure showing links between the environment, community dynamics, ecosystem functioning and ecosystem services, as well as the role of species traits in mediating ecosystem functioning. Taken with permission from Hooper et al. (2005).

1.5. Methodology

1.5.1. Choosing bio-ecological traits

Selecting traits is both difficult and subjective because no universal agreement exists on trait number or importance, and because trait choice has a profound impact on research conclusions (Lefcheck et al., 2015; Leps et al., 2006; Zhu et al., 2017). However, some authors have proposed guidelines for appropriately choosing traits. For instance, Dee et al.

(2016), Mcgill et al. (2006), and others have stated that trait choice should be explicitly based on the hypotheses of interest, while Botta-Dukát, (2005), Fonseca and Ganade (2001), and

Petchey and Gaston (2006) have also stated that traits should reflect well-defined ecological functions. Lefcheck et al. (2015) argued that only multi-trait approaches can holistically capture functional diversity, and that multiple traits are needed to evaluate the processes shaping communities. Indeed, studies have shown that the ability to predict community dynamics increases sharply with an increasing number of traits (Frenette‐Dussault et al.,

2013; Shipley et al., 2011). However, while predictive power increases with the number of traits, Laughlin (2014) found that predictive power saturated at roughly eight traits. Laughlin

(2014) and Rosado et al. (2013) therefore stated that trait choice should maximize dimensionality while minimizing trait correlation in order to maximize explainable variation among species. Some authors also argue that depending on the research object, it may be necessary to distinguish between response and effect traits in order to draw meaningful conclusions (Pillar et al., 2013).

As the objective of this thesis was to examine the functional responses of fish communities to environmental variations in time and space, I focused primarily on response traits. However, it should be noted that many traits qualify as both response and effect traits in marine fishes. I thus began by searching the literature to identify traits previously used to

46 examine fish community dynamics and compiled a list of traits potentially implicated in fish community responses to environmental gradients. I then obtained as many traits as possible by accessing published literature and open-access databases like FishBase (Froese and Pauly,

2012). This initial procedure resulted in over twenty potential traits. I next removed redundant traits that described similar biological or ecological information. For example, while body length at maturity and maximum body length are separate traits, they are highly correlated and describe approximately the same information. Following guidelines published in the literature, I reduced the initial list of ecological traits to 10 traits that characterize a wide range of biological and ecological aspects of fishes, and that may be implicated in fish responses to environmental gradients based on previous literature (Table 1). Traits chosen describe life history, trophic ecology, and habitat preferences. These included length at maturity, age at maturity, fecundity, offspring size, parental care, trophic level, trophic guild, water-column position, temperature preference, and substrate preference. However, not all traits were used for each individual study within this thesis, as trait choice was modified according to each study’s specific objectives and hypotheses.

Table 1 | Ecological traits used to examine fish community functional structure along with the reasoning and references for choosing each trait.

Bio-ecological Category Reasoning References Trait Influences growth rate, Length at (Brown et al., 2004; Fisher et al., 2010; Life history metabolism, feeding rate, maturity Kleiber, 1947; Petchey et al., 2008) mobility, position in food web. Influences growth rate, speed of (Crozier and Hutchings, 2014; King and Age at Life history maturation and reproduction, McFarlane, 2003; Mims and Olden, maturity population turnover. 2012; Pankhurst and Munday, 2011) Influences population growth (Lambert, 2008; Pecuchet et al., 2017, Fecundity Life history rate, dispersal rate, population 2018; Pörtner et al., 2001) turnover. Determines offspring survival and (Adams, 1980; Pianka, 1970; Sirot et al., Offspring size Life history dispersal. 2015; Ware, 1975) Determines offspring survival and (Gross and Sargent, 2015; Smith and Parental care Life history dispersal. Represents an energetic Wootton, 1995; Winemiller and Rose, trade-off in life history strategy. 1992) Influences position within food (Gravel et al., 2016a; Hempson et al., Trophic level Trophic ecology web, impacts on carbon and 2018; Huxel and McCann, 1998; nutrient fluxes. Schneider et al., 2016) Influences distribution, (Albouy et al., 2011; Duffy et al., 2007; population growth rate, Trophic guild Trophic ecology Finke and Denno, 2005; Houk et al., population size, impacts on 2017) carbon and nutrient fluxes. (Alheit et al., 2014; Lindegren et al., Water column Habitat Influences distribution, dispersal, 2012; Montero-Serra et al., 2014; position preference mobility. Rijnsdorp et al., 2009) Temperature Habitat Influences distribution, niche (Cheung et al., 2013; Day et al., 2018; preference preference breadth, environmental tolerance Dulvy et al., 2008; Givan et al., 2018) Influences distribution, niche Substrate Habitat (Costello et al., 2015; Dencker et al., breadth, impacts on carbon and preference preference 2017; Stuart-Smith et al., 2013) nutrient fluxes.

1.5.2. Functional space

Many of the studies in this thesis used a multi-dimensional functional space to characterize fish community functional structure. The functional space is a multi-dimensional ordination that represents the functional similarity among all species in the community (Maire et al.,

2015; Mouillot et al., 2013b; Villéger et al., 2008). Functional space is created by first computing a distance matrix that quantifies the dissimilarity between all pairs of species based on their trait combinations (see Figure 2). In this thesis, Gower’s distance measure was used to build distance matrices because Gower’s distance can evaluate mixed data types (i.e., both continuous and categorical) and handle missing data (Gower, 1971; Villéger et al., 2008).

These criteria are essential for trait-based studies as many different trait types are combined, such as body length and diet, and oftentimes trait values are not available for all species. The

Gower distance matrix is then converted into a functional space using Principal Coordinates

Analysis (PCoA), an ordination method that transforms pairwise species distances into a multi-dimensional space that best represents the overall variation among species (Borcard et al., 2011). This procedure creates a functional space where all species in the community are positioned according to their trait dissimilarity, and the main axes of the space are associated with the traits that explain the greatest amount of variation among species (Figure 2).

Functional spaces provide an exceptional tool for examining community dynamics. For example, the spatio-temporal dynamics of communities can be examined within the functional space by examining the mean position of the community within the functional space (i.e., community centroid) and how this position changes along environmental gradients or over time.

Figure 2 | Conceptual diagram showing the procedure for constructing a multidimensional functional space.

1.5.3. Community weighted means

Another practical approach for characterizing functional community structure is to examine the average trait values for the overall community, i.e., the community-weighted means

(CWMs). CWMs are calculated as the abundance-weighted average trait values of all species in a given community. CWMs allow evaluating how trait structure varies in space and time, as changes in species composition and abundance lead to changes in the mean trait values of the community. For example, the loss of a large-bodied species in the community will lead to a reduction in average body size. CWMs are often used for examining changes in individual traits, however, they are also appropriate for a variety of multivariate analyses, such as ordinations, and are highly practical for examining communities’ functional dynamics. For

50 example, in several studies in this thesis, I examined temporal community dynamics by applying ordination techniques such as Principal Components Analysis (PCA) to CWM trait values.

1.6. Study ecosystems

This thesis focused on three interconnected ecosystems with distinct spatial scales, physical environments, and levels of taxonomic and functional diversity: the North Sea, Eastern

English Channel, and Bay of Somme (Figure 3). These three ecosystems provide an excellent opportunity to examine the spatial and temporal dynamics of fish communities because they have been surveyed annually by scientific monitoring campaigns over the last three decades.

Within each monitoring campaign, an array of spatial sites are evaluated using bottom trawls and all fishes are identified and counted, providing a holistic view of community structure

(the survey design and data structure of each of these campaigns are presented in the following chapters). These scientific monitoring campaigns were initially developed for stock assessments; however these data have been relatively unexploited for biodiversity assessments and these ecosystems provide a unique opportunity to compare findings and identify common patterns across spatial scales.

Figure 3 | Map showing the three study ecosystems and their respective spatial scales.

1.6.1. North Sea

The North Sea is a semi-enclosed marginal sea of the Atlantic Ocean located on the northwest

European continental shelf (Kenny et al., 2009; Quante et al., 2016). The North Sea, including its coastal habitats, covers over 750,000 km2, while the main basin spans approximately 500

52 km from east to west and 900 km from north to south (Kenny et al., 2009; Quante et al.,

2016). The North Sea is highly depth-structured with a distinct shallow southern zone and a deep northern zone roughly divided by the 50-m depth contour (Kenny et al., 2009; Quante et al., 2016). The southern North Sea has a mean depth of around 30 m, while the north has a mean depth of around 100 m. The physical environment differs greatly between these two areas. The southern North Sea has higher freshwater and nutrient input because it encompasses several large estuaries that drain basins in northern Europe and the United

Kingdom. Consequently the southern North Sea has lower salinity and higher primary productivity. Additionally the southern North Sea is continuously well-mixed by tidal currents while the northern North Sea is seasonally stratified (Kenny et al., 2009; Quante et al., 2016).

Mean annual temperatures in the southern North Sea range from about 10.5-12.5°C, while the northern North Sea ranges from about 9.5-10.5°C, and there is a strong north-south annual temperature gradient. However, the North Sea exhibits a unique winter temperature shift, where ocean currents bring warm North Atlantic water into the northwest and the temperature gradient temporally reverses (Dulvy et al., 2008).

The North Sea has a rich biodiversity with over 200 species of fish, providing important fishing grounds for nine different countries. The ecosystem produces 10 million tons of fish and shellfish biomass per year, 25% of which is removed by commercial fisheries

(Quante et al., 2016). In recent years the ecosystem has been characterized by high abundances of Norway pout, herring, sprat, whiting, and dab. Historically the North Sea was also characterized by a large abundance of gadoids like haddock and cod; however, overfishing has led to a major decrease in size composition and trophic level in the fish community (Daan et al., 1990; Quante et al., 2016; TWAP, 2015; van Gemert and Anderson,

2018). In the first half of the 19th century North Sea fish stocks were heavily overfished

(TWAP, 2015). Total commercial landings increased sharply in the late 1800s and continued

53 to rise until peaking in the 1970s (Figure 4a) (ICES, 2018; TWAP, 2015). However, despite declining total landings, fishing mortality remained high for many demersal species into the

1980s and 1990s (Figure 4b). For example, cod landings peaked in the 1980s and declined progressively thereafter (ICES, 2018). Yet, following the drastic decline of Atlantic herring in the 1970s and progressive declines in many other stocks through the 1980s and 1990s, more conservative fishing regulations were enacted and commercial landings have steadily declined in recent decades. In recent years (particularly after 2003) exploitation levels have continued to decrease and cooperation between science and industry has improved, leading to recovery of important stocks such as cod, hake, and herring (ICES, 2018; van Gemert and Anderson,

2018).

Figure 4 | Total commercial fisheries landings in the Greater North Sea by country (a) and by stock type (b) from 1950 – 2017. Taken with permission from ICES (2018).

The North Sea has also been heavily impacted by climate in recent decades, and is considered a global warming hotspot, having warmed by 1.3°C between 1982 and 2006

(Simpson et al., 2011). Multiple studies have reported northward distribution shifts in fishes as well as shifts toward deeper waters (Dulvy et al., 2008; Engelhard et al., 2014, 2013; ICES,

2016; Perry et al., 2005). As the North Sea has warmed, there has also been an increase in total species richness and abundance. For instance, Hiddink and Ter Hofstede (2008) found an increase in species richness from 1985-2006 that was correlated with increasing temperatures, and Simpson et al. (2011) found positive relationships between temperate and abundance changes for 27 of the 50 most abundant species. These increases have been primarily in smaller-bodied species, which has reinforced the long term shift toward smaller, lower-trophic level fishes. Beyond fish communities, substantial changes in other biotic compartments have been observed in the North Sea. For instance Beaugrand (2004) documented a regime shift in plankton community structure in the late 1980s that was attributed to an inflow of oceanic currents and a rapid increase in ocean temperatures associated with the NAO.

1.6.2. Eastern English Channel

The English Channel forms a trench between Northern France and the United Kingdom that connects the Atlantic Ocean to the southern North Sea. The Eastern English Channel (EEC) is

560 km long and between 30 and 240 km wide, covering a total area of approximately 75,000 km2. The EEC has a strong west-east depth gradient, with a maximum depth of around 70 m near the Cotentin Peninsula, and a minimum of around 40 m in the Dover Straight (Martin et al., 2009). The EEC is highly influenced by tidal currents, with a tidal range up to 12 m (Idier et al., 2012), causing the ecosystem to be well-mixed year-round (Martin et al., 2009). Mean annual sea surface temperatures range from roughly 12°C to 13°C and follow a seasonal depth

55 gradient where the shallow coasts are warmer in summer but colder in winter (Martin et al.,

2009). Salinity is highest in the central Channel where Atlantic waters penetrate, while a coastal river of low-salinity water runs along the French coast (Martin et al., 2009).

The English Channel has a rich biodiversity because it services as a transition zone for warm and cold Atlantic waters and has high primary productivity due to land-based nutrient loading (Martin et al., 2009). In recent decades the ecosystem has been characterized by high abundances of horse mackerel, poor cod, pouting, sprat, and mackerel. Like the North Sea, the

English Channel was subjected to high fishing pressure in the 20th century that led to a long- term shift from large demersal species like cod and haddock to smaller-bodied species and shellfish (Molfese et al., 2014). For instance, in the Western English Channel, Genner et al.

(2010) found a decrease in the size distribution and abundance of large targeted species in response to commercial fishing pressure, while McHugh et al. (2011) found large decreases in slow-maturing species such as sharks and rays.

The EEC has rapidly warmed in recent decades, and has been particularly impacted by natural climate oscillations like the North Atlantic Oscillation (NAO) and Atlantic

Multidecadal Oscillation (AMO). Climate oscillations are large-scale cycles in atmospheric and oceanic conditions that affect weather patterns and temperatures. The AMO in particular is a 60-80 year cycle affecting North Atlantic sea surface temperatures. Climate oscillations are known to drive population changes in both fish and plankton, and in the English Channel the AMO has been previously linked to the Russel Cycle – a decadal shift in plankton composition that follows warm and cool conditions (Coombs and Halliday, 2011). Auber et al. (2015) found a major shift in the taxonomic structure of fish communities in the EEC in the late 1990s in relation to a warming phase of the AMO that lead to rapid increases in sea temperatures. This shift was characterized by a pronounced decline in the abundance of dominant species such as horse mackerel, poor cod, sprat, pouting, and whiting (Auber et al.,

2015). Additionally, some warm-water species have increased in abundance and a few

‘Mediterranean’ species such as common pandora and gilt-head bream have made their way into the Channel in the last two decades (Ifremer unpublished data). Similar findings have been reported in the Western Channel, for instance Hawkins et al. (2003) found an increase in warm-water fish and plankton species following an increase in sea surface temperatures between 1980 and 2000.

1.6.3. Bay of Somme

The Bay of Somme is the second largest estuary in the Eastern English Channel, covering an area of 72 km2. The Bay of Somme drains six river channels, but is primarily formed by the mouth of the Somme River. As an estuary, the Bay has strong seaward gradients in salinity, depth, and bed shear stress. The Bay is an important nursery ground for several important commercial species like plaice, sole, and herring (Auber et al., 2017a; Le Pape, 2005;

Rybarczyk et al., 2003). Yet, few studies have examined fish community dynamics in the Bay of Somme. The Bay of Somme currently has no commercial fishery and has had no major fishing activity since the 1970s, (Dreves et al., 2010). However, like other North Atlantic ecosystems, the Bay of Somme has undergone rapid warming in recent decades, and there have been climate-induced changes in fish community structure. Most notably, Auber et al.

(2017a) documented a major decrease in fish species with cold temperature preference in the bay following an increase in the AMO that lead to rapid sea surface warming. In particular there were major declines in gobies, dragonet, dab, and plaice (Auber et al., 2017a).

1.7. Thesis objectives

Although fisheries monitoring efforts are globally intensive, identifying the underlying mechanisms structuring fish communities remains challenging, as community structure is highly variable due to both natural environmental changes and human disturbances (Anderson et al., 2008; Hsieh et al., 2006; Lehodey et al., 2006). Furthermore, despite decades of extensive data collection on fish abundance and distribution in many regions worldwide, the functional responses of communities to environmental change and human pressures have been poorly investigated (Dulvy et al., 2008; Micheli and Halpern, 2005; Somerfield et al., 2008).

While data from many fisheries monitoring campaigns are extensively used to assess commercial stocks, they have been largely underused to assess functional biodiversity. Yet, these rich data sources provide important opportunities to examine the spatio-temporal dynamics of fish functional structure in relation to environmental change. Using long-term scientific monitoring surveys to examine fish functional structure is thus an important step forward in understanding how fish communities respond to environmental changes in space and time. Therefore, based on the ecosystems of the North Sea, Eastern English Channel, and

Bay of Somme, the aims of this thesis were to describe the spatio-temporal dynamics of fish community functional structure over last 25-30 years and to identify the main drivers and their mechanisms of effect.

2. Ecological and life-history traits explain a climate-induced shift in a

temperate marine fish community2

2Published as McLean M., Mouillot D., Auber A. 2018. Ecological and life-history traits explain a climate-induced shift in a temperate marine fish community. Marine Ecology

Progress Series 606:175-186 60

2.1. Preface

Prior to the start of this thesis, Auber et al. (2015) documented a major shift in fish community structure in the Eastern English Channel (EEC) ecosystem. The study by Auber et al. (2015) was completed using a taxonomic approach, evaluating community changes on a species-by-species basis and identifying the species with the greatest contributions to the overall change. Auber et al. (2015) et al found that the taxonomic community shift was primarily characterized by a pronounced and rapid decrease in overall fish abundance, with major decreases in the most abundant species such as Trachurus trachurus, Trisopterus minutus, Sprattus sprattus, and Trisopterus luscus, but with minor increases in a few rare species such as Scylorhinus stellaris, Mustelus asterias, Diacenturs labrax, and Raja brachyura. As the main objective of this thesis was to evaluate fish community dynamics using a functional approach, the findings of Auber et al. (2015) provided an excellent starting- point for this work. I therefore began my thesis research by re-examining the taxonomic shift in EEC fish communities from a trait-based perspective to identify functional similarities among species with major changes in abundance, to characterize overall changes in fish functional structure, and to identify associations between fish functional traits and environmental factors. Additionally, this chapter benefitted from a prior study by Auber et al.

(2017b) that demonstrated how the multivariate analysis, Principal Response Curves (PRC), could be modified to simultaneously examine temporal and spatial changes in community structure. Thus, in the first chapter of this thesis, I used the PRC analysis to examine the spatio-temporal changes in fish functional structure in the EEC.

2.2. Introduction

Sustainably managing natural resources requires a greater understanding of community dynamics and ecosystem functioning under changing environmental conditions (Carpenter et al., 2009; Cheung et al., 2016; Kremen, 2005). Examining community dynamics on a species by species basis is useful for stock assessments and population management; however, such taxonomic approaches are less insightful for understanding ecological mechanisms and processes (Dıaź and Cabido, 2001; Mcgill et al., 2006; Mouillot et al., 2013b). By contrast, trait-based ecology links community dynamics and ecosystem processes by identifying how organismal traits respond to environmental change (Gross et al., 2017; Mcgill et al., 2006;

Winemiller et al., 2015), which can generate predictive relationships that are not taxon or ecosystem specific (Dı́az and Cabido, 2001; Lavorel and Garnier, 2002; Winemiller et al.,

2015). This approach has gained progressive support and is now recognized as an essential step forward in community ecology and natural resource management (Hooper et al., 2005).

Thus, a better understanding of community dynamics can be achieved by describing how community functional structure responds to environmental factors over both time and space, and identifying which ecological traits best mediate community responses.

Rapid and pronounced shifts in community structure have been documented in many ecosystems worldwide and are often related to rapid environmental change (Beaugrand, 2004;

Scheffer and Carpenter, 2003; Vergés et al., 2014; Wernberg et al., 2016). In fish communities, such shifts have been documented in response to extreme climatic events and climate cycles that alter sea surface temperatures and oceanographic processes. For example,

Wernberg et al. (2016) documented an increase in subtropical fishes following a heatwave along southwestern Australian and Reid et al. (2001) documented an increase in horse mackerel landings following a phase change of the North Atlantic Oscillation. Fluctuations of sardine and anchovy landings have also been linked to alternating cycles of the Pacific

Decadal Oscillation (Chavez et al., 2003; Lindegren et al., 2013). Yet, such community shifts are classically examined using taxonomic approaches, which are limited in explaining biological responses and their consequences for ecosystem functioning (Chavez et al., 2003;

Clare et al., 2015; Reid et al., 2001). Auber et al. (2015) previously documented a rapid shift in the Eastern English Channel fish community in response to a warming phase of the Atlantic

Multidecadal Oscillation (AMO), which induced substantial decreases in the abundance of several dominant species and moderate increases in some subordinate species. Such taxonomy-based findings can describe major ecosystem changes with potential impacts on ecosystem services but cannot identify the functional mechanisms that underpin how and why certain organisms respond strongly to climatic changes while others are unaffected. Rather, greater understanding of fish community responses to rapid environmental change could be achieved by functionally characterizing community shifts and identifying the most responsive functional groups.

While a better understanding of community shifts can be achieved by describing spatio-temporal changes in functional groups, statistical methods for such studies are limited

(Leps et al., 2006; Petchey and Gaston, 2006; Violle et al., 2007). Auber et al. (2017b) recently proposed a new application of Principal Response Curves (PRC) for examining community shifts between time frames. PRC is a multivariate method that simultaneously describes spatial and temporal changes in community structure and identifies the most responsive species (Auber et al., 2017b; Van den Brink and Ter Braak, 1999). Despite the utility of PRC for describing community shifts, this method has been largely underused and has not yet been applied to ecological traits.

Here we used a trait-based approach with PRC to functionally characterize the shift in the Eastern English Channel fish community, specifically answering i) did changes in taxonomic community structure correspond to a pronounced shift in functional structure, ii)

63 which functional groups were most contributive to overall changes in functional structure, and iii) which environmental factors were most associated to changes in functional structure through time? By examining the underlying functional changes behind the taxonomic shift, we provide a mechanistic understanding of changes in fish functional structure in response to rapid environmental change.

2.3. Materials and methods

2.3.1. Fish community data

The fish community of the Eastern English Channel (EEC, area VIId defined by the

International Council for the Exploration of the Sea, ICES) has been sampled every October since 1988 during the Channel Ground Fish Survey (CGFS). Here, we focused on the study period of 1988 – 2011. The CGFS sampling scheme is spatially stratified by subdividing the

EEC into 15’ latitude × 15’ longitude survey zones where at least one 30-min haul is made during daylight hours at an average speed of 3.5 knots. A high (3 m) vertical opening bottom trawl (GOV) with a 10-mm-stretched-mesh-size codend is used. The stratified sampling scheme manages to complete 90 to 120 hauls per year depending on weather conditions, and we removed all sites that had not been visited for at least three consecutive years (Auber et al.,

2017b). After each haul, all captured fishes are identified and the number of individuals per species is counted. Abundance indices at each sampling station were obtained from the ICES data portal and were standardized to numbers of individuals per km2 (ICES).

2.3.2. Ecological traits

Ecological trait data for 73 taxa (67 species and 6 genera not identified to species level) were collected from FishBase (Froese and Pauly, 2012), the Ocean Biogeographic Information

System (http://www.iobis.org/), the Global Biodiversity Information Facility

(https://www.gbif.org/), Engelhard et al. (2011), Pecuchet et al. (2017), and a search of primary literature. Nine ecological traits were used for this study related to life history, habitat use, and trophic ecology (Table 1). Traits were chosen if they were i) readily available, ii) deemed accurate by comparison of multiple sources and iii) potentially implicated in the response of communities to environmental change. Categorical or binary traits included parental care, water column position, and trophic guild, while continuous traits included length and age at maturity, fecundity, offspring size, temperature preference, and trophic level. Temperature preference was calculated as the median temperature of a species across its global range of observations for which data were available.

Table 1 | Ecological traits and their corresponding functional groups (i.e., trait attributes).

Ecological Trait Functional (Trait) Groups

Length at Maturity 2.65-11.03, 11.04-18.69, 18.70-26.63, 26.64-40.13, ≥40.14 (cm)

Age at Maturity 0.33-1.39, 1.40-2.31, 2.32-2.99, 3.0-4.49, ≥4.5 (years)

Parental Care Pelagic Egg, Benthic Egg, Clutch Hider, Live Bearer

Offspring Size (mm) 0.34-0.89, 0.90-1.09, 1.10-1.39, 1.40-2.67, ≥2.68

Fecundity 2-879, 880-9999, 10000-106399, 106400-406589, ≥406590

Water Column Demersal, Reef-Associated, Benthopelagic, Pelagic Position

Temperature 4.62-10.41, 10.42-11.29, 11.30-11.72, 11.73-12.49, ≥12.5 Preference (°C)

Trophic Guild Detritivore, Planktivore, Benthivore, Carcinophage, Benthopiscivore, Piscivore

Trophic Level 2.2-3.23, 3.24-3.39, 3.4-3.69, 3.7-3.99, ≥4

When combining species’ abundances and traits, ecologists generally have two choices: calculate community-weighted mean (CWM) trait values or use the abundances of functional groups. The advantage in using CWM trait values is that continuous data are not broken into categories and thus no information is lost, however examination of the underlying changes in trait values is not possible. For instance, CWM trait values could reveal that, on average, maximum length of fishes decreased over time, but could not reveal whether this was driven by an increase in small fishes, a decrease in large fishes, or both. In order to characterize the EEC fish community shift in terms of changes in the actual abundance of different functional groups, we categorized continuous traits, making all traits categorical.

Continuous traits were therefore put into five groups by using quintiles of the continuous trait data for all individuals with each trait (Table 1). The abundances of all functional groups (i.e., each trait category or attribute) were then ln(Ax+1) transformed (where A*min(x)=2) (Auber et al., 2017b; Van den Brink et al., 2000).

2.3.3. Environmental factors

Environmental factors included both ocean-wide climate oscillations and local environmental parameters. The North Atlantic Oscillation (NAO) is an intra-decadal alternation of atmospheric mass over the North Atlantic, which is known to influence sea surface temperatures (SST) and oceanographic processes (Dickson, 2000). The NAO index used here is based on the difference of normalized sea-level atmospheric pressure between Lisbon,

Portugal and Reykjavik, Iceland (Dickson, 2000). The annual NAO index for the period

1988–2011 was obtained from NOAA (https://www.ncdc.noaa.gov/teleconnections/nao/). The

Atlantic Multidecadal Oscillation (AMO) refers to a 60–80 year cycle of North Atlantic SST

(Edwards et al., 2013). The AMO index is computed as a monthly area-weighted mean of

SST anomalies over the North Atlantic (from 0 to 70°N), which is detrended to remove the effect of global warming. This index is correlated to air temperature and rainfall over the

Northern hemisphere, and positive phases are associated with warm, dry conditions, while negative phases are associated with cool, wet conditions (Enfield et al., 2001). AMO values were also derived from NOAA, US (https://www.esrl.noaa.gov/psd/data/timeseries/AMO/).

Local environmental parameters included SST, chlorophyll-a, salinity, and dissolved oxygen. Mean annual SST data were derived from the Hadley Centre for Climate Prediction and Research’s freely available HadISST1 database (Rayner et al., 2003). Mean annual chlorophyll-a data came from the Sir Alister Hardy Foundation for Ocean Science’s

Continuous Phytoplankton Recorder database (SAHFOS). Surface salinity and dissolved oxygen were extracted from outputs of the NORWegian ECOlogical Model

(http://www.ii.uib.no/~morten/norwecom.html, (Skogen et al., 1995). NORWECOM is a coupled 3D physical, bio-chemical model for the North Sea and the English Channel that provides monthly averages of environmental parameters at a geographical resolution of 0.1 degree. For salinity and dissolved oxygen, data were averaged across months and spatial locations to obtain mean annual values for the entire EEC. Non-algal suspended matter was obtained from satellite data (Gohin, 2011) for each survey, which were then averaged across years to obtain mean annual values.

2.3.4. Fishing pressure

Fishing pressure was assessed using three different fishing mortality indices: Fpelagic, Fdemersal, and Fbenthic, for pelagic, demersal and benthic species, respectively. These were estimated annually as the 1-year-lagged landing-weighted average fishing mortality rates for stocks assessed by ICES working groups, namely mackerel and herring for pelagic, cod and whiting for demersal, and plaice and sole for benthic. The fishing mortality rates of these 6 stocks (the 67 only stocks analytically assessed in the EEC) were considered representative of the global fishing pressure on the EEC fish community, as these species account for more than 60% of total landings in the EEC (Auber et al., 2015). The 1-year lagging accounted for the fact that annual instantaneous fishing mortality rates of a given year are expected to affect the abundance of fish stocks the year after (Auber et al., 2015). Each fishing mortality index was calculated as the average fishing mortality of the two corresponding stocks weighted by their landings. We considered pelagic, demersal and benthic fishing mortality to account for mixed types of fishing gear, and to encompass the totality of fishing pressure throughout all habitat zones in the EEC. The EEC is a mixed-gear fishery where pelagic stocks are generally targeted by midwater trawls and demersal and benthic stocks are targeted by a mix of otter trawls, beam trawls, nets, pots, and dredges (Pascoe and Coglan, 2002; Ulrich et al., 2002).

Thus by computing three different fishing mortality indices, we account for fishing pressure across several stocks and gear types, within the limitations of ICES-assessed stocks. Fishing mortality rates of the different stocks, as well as landing statistics, were extracted from the

ICES Stock Assessment Summary database and Catch Statistics database

(http://www.ices.dk/marine-data/dataset-collections/Pages/Fish-catch-and-stock- assessment.aspx).

2.3.5. Principal Response Curves

Temporal and spatial changes in fish functional structure were assessed using Principal

Response Curves (PRC). PRC is a special case of partial redundancy analysis (pRDA) with a single tested factor as the explanatory variable and a single dimension of repeated observations as the co-variable (Van den Brink and Ter Braak, 1999). Auber et al. (2017b) recently adapted the PRC analysis to examine spatiotemporal changes in community structure, specifically between two time periods (i.e., a ‘baseline period’ and a ‘tested 68 period’), by using spatial sites as repeated observations and time as a tested factor. The PRC analysis generates canonical regression coefficients (cdt) for each sampling site, as well as contribution weights (bk) for each species (or functional group in this study). The absolute values of canonical coefficients cdt quantify, at each sampling site, the magnitude of change between the two tested time periods, and the absolute values of functional group weights bk quantify the contribution of each functional group to the overall change of community structure; groups with weights near zero have little or no response, while groups with high weights have strong responses. For a given functional group; the sign (+/-) of the canonical coefficients cdt indicates the type of community response and is interpreted by comparing with the sign (+/-) of weights bk. When the signs of bk and cdt coefficients are identical, the abundance of the corresponding functional group is higher in the tested period than the baseline period, and when the signs of bk and cdt are opposite the abundance is higher in the baseline period than the tested period. For a complete description of the original PRC method and the new application of the PRC, see Van den Brink and Ter Braak (1999) and Auber et al.

(2017b).

In correspondence with the taxonomic community shift in the EEC (Auber et al.,

2015), we considered the years 1988 to 1997 as the pre-shift period (baseline period), and

1998 to 2011 as the post-shift period (tested period). We then applied the PRC analysis to examine changes in functional community structure at each site between the two time periods, and to identify the functional groups with the highest contributions to overall change. The

PRC analysis was performed using the function prc in the R package vegan. Significant changes in functional structure between the two periods were then tested at each sampling site using Monte-Carlo permutation tests designed to correct for the increase in the family-wise type 1 error rate due to multiple comparisons across sampling sites (see Auber et al. (2017b) for full details and R code).

2.3.6. Influence of environmental factors and fishing mortality

We identified the influences of environmental factors on temporal changes in fish functional structure using RLQ and fourth corner analyses, where we considered environmental factors across years rather than across sites. RLQ is a method that integrates environmental data (R), species abundance data (L), and species’ ecological traits (Q) to examine how environmental factors influence trait variation (Dray et al., 2014). RLQ examines the co-inertia between three separate ordination analyses (i.e., R, L, and Q), while fourth-corner analysis assess statistical associations between each environmental factor and each functional group individually (Dray et al., 2014; Dray and Legendre, 2008). Thus RLQ analysis was first used to reveal major environmental drivers of temporal variation in fish functional structure, while fourth corner analysis then identified significant correlations between individual functional groups and environmental factors. For both the RLQ and fourth corner analyses, species abundance data were the mean time series of species composition averaged over the entire

EEC, thus the species abundance table (L) consisted of species mean abundances in columns and years in rows. While RLQ is generally applied to spatial data, as the analysis functions through co-inertia of three individual ordinations, alternative species abundance structures, including temporal, are permissible (see Dolédec et al. (1996) and Dray and Legendre

(2008)). Temporal environmental drivers included in RLQ analysis were mean annual AMO,

NAO, SST, salinity, chlorophyll-a, oxygen, and fishing mortality (pelagic, demersal, and benthic). Thus the environmental table consisted of mean environmental factors in columns and years in rows. A potential concern with RLQ and fourth corner analysis was the influence of temporal autocorrelation among variables. Autocorrelation can bias statistical tests by inflating type-1 error, leading to spurious correlations. However, RLQ analysis is purely descriptive and does not test for significant relationships (see Thuiller et al. (2006b)). To account for potential autocorrelation in fourth corner analysis, which does test for significant

70 relationships, we used both the standard fourth corner analysis and an extended version of fourth corner that integrates Moran spectral randomization to account for autocorrelation

(Wagner and Dray, 2015). MSR is a constrained randomization procedure that compares observed values against a null model that preserves the autocorrelation of the data (Wagner and Dray, 2015). Due to missing environmental data in 2009, 2010, and 2011, RLQ and fourth corner analysis were calculated for the time series 1988 – 2008.

2.4. Results

The PRC analysis revealed that sampling sites explained 34% of spatio-temporal variance in fish functional structure (horizontal axis Figure 1a), while time explained 13.4%, 71% of which is represented by the first canonical axis of the PRC analysis (vertical axis Figure 1a).

All sites in the EEC were characterized by positive cdt values, indicating that the type of community change was the same at every site (Figure 1a). However, cdt values were highly variable across sites, indicating that while all sites experienced the same type of change, the magnitude of change was spatially heterogeneous (Figure 1a,b). Monte-Carlo permutation tests further revealed that 36 out of 79 sites had a significant change in fish functional structure between the two time periods (Figure 1b).

Figure 1 | Principal Response Curve (PRC) showing changes in fish community structure across sampling sites between the baseline period [1988-1997] (pre-shift) and tested period [1998-2011] (post-shift), with the most contributive functional groups ranked by their bk coefficients. For clarity, only traits with bk coefficients in the first or last decile are shown. b) Map showing the amplitude of temporal changes in fish functional structure (i.e., cdt values) at each sampling site and the trend (increase or decrease) of abundance for each functional group. Sampling sites with significant change are shown by asterisks (***: p < 0.001; **: 0.001 < p < 0.01; *: 0.01 < p < 0.05).

Functional group weights bk revealed that changes in fish functional structure were primarily driven by decreases in species with small offspring size (0.9 – 1.1 mm), pelagic egg release

(broadcast spawning), planktivory, pelagic water column position, and low age at maturity

(1.4 – 2.3 years), as the absolute values of bk were much higher for functional groups with negative values (decreasing) (Figure 1). Concurrently, there was an increase in clutch hiders

(i.e., high parental care) and species with high age at maturity (3 – 4.5 years), moderate

72 trophic level (3.25 – 3.4), high temperature preference (≥12.5°C), and low fecundity (2 – 880)

(Figure 1). Thus, the taxonomic shift in the EEC fish community in the late 1990’s was generally characterized by a strong decrease in pelagic and planktivorous species with opportunistic, ‘r-selected’ life history traits, and a moderate increase in species with equilibrium, ‘K-selected’ life history traits.

2.4.1. Influence of environmental factors

RLQ analysis identified AMO, demersal and pelagic fishing mortality, SST, and NAO as the primary drivers of temporal variability in fish functional structure, as AMO and fishing mortality had the highest correlations with the first RLQ axis, and SST and NAO were both highly correlated with the first RLQ axis and had the highest correlations with the second

RLQ axis (Figure 2, Figure 3).

Figure 2 | RLQ biplots showing temporal (a) associations between environmental factors (b) and functional groups (c). For clarity, only functional groups with the greatest correlations (first or last decile) to the first and second RLQ axes are plotted (b). Environmental factor acronyms: Atlantic Multidecadal Oscillation (AMO), sea surface temperature (SST), chlorophyll-a (Chl-a), pelagic fishing mortality (Fpelagic), demersal fishing mortality (Fdemersal), benthic fishing mortality (Fbenthic). Temp. pref. = temperature preference. The first two RLQ axes preserved 87% of environmental variation and 71% of trait variation.

Figure 3 | Contribution of environmental factors to temporal variation in fish functional structure according to Pearson correlations between environmental factors and the first (a) and second (b) axes of the RLQ analysis. Environmental factor acronyms: Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), sea surface temperature (SST), chlorophyll-a (Chl-a), pelagic fishing mortality (Fpelagic), demersal fishing mortality (Fdemersal), benthic fishing mortality (Fbenthic).

Initial fourth corner analysis, without MSR, indicated that AMO had positive relationships with demersal species and species with high age at maturity (3 – 4.5 years), and negative relationships with pelagic and planktivorous species and species with low trophic level (2.2 – 3.25), low age at maturity (1.4 – 2.3 years), and low temperature preference (10.4

– 11.3°C) (p < 0.05, Figure 4). In contrast, NAO was positively correlated with planktivores and negatively correlated with species with high temperature preferences (≥12.5°C). SST was positively correlated with species with the largest length at maturity (≥40.1 cm), lowest fecundity (2 – 880), largest offspring size (≥2.7 cm), and reef-associated species, and negatively correlated with species with low temperature preference (10.4 – 11.3°C) (p < 0.05,

Fig 4). Dissolved oxygen had a single, positive association with high trophic level species

(≥4) (p < 0.05, Fig 4). Pelagic and demersal fishing mortality had nearly identical relationships with functional groups; however pelagic mortality had fewer significant

74 associations. Both pelagic and demersal mortality were positively associated with pelagic and planktivorous species, and negatively associated with demersal species and species with high age at maturity (3 – 4.5 years) (p < 0.05, Fig 4). Demersal fishing mortality was also positively related to species with the lowest trophic level (2.2 – 3.25) and lowest age at maturity (1.4 – 2.3 years) (p < 0.05, Fig 4). However, re-running the fourth corner analysis with MSR to account for temporal autocorrelation revealed that only AMO and SST had significant associations with any of the functional groups, indicating potential spurious correlations for fishing mortality, oxygen, and NAO due to high autocorrelation (Fig 4). The relationships between AMO, SST, and functional group dynamics remained nearly identical, with only the associations between AMO and low trophic level, and between SST and low temperature preference no longer significant.

Figure 4 | Results of fourth corner analyses of trait-environment correlations without (a) and with (b) Moran spectral randomization (white = no significant relationship, dark grey = positive relationship, light grey = negative relationship). Environmental factor acronyms: Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), sea surface temperature (SST), pelagic fishing mortality (Fpelagic), demersal fishing mortality (Fdemersal). Temp. pref. = temperature preference.

2.5. Discussion

Here, we found that a previously documented taxonomic shift in a temperate marine fish community also corresponded to a considerable shift in fish functional structure. This is a major finding as community functional structure can be relatively unaffected by temporal species replacement or turnover (Clare et al., 2015; Villéger et al., 2010). Examining the taxonomic shift through the lens of ecological traits provided more pronounced results and greater insight into the biological mechanisms behind the shift. We found that the shift was characterized by a large decrease in the abundance of pelagic, planktivorous species with low temperature preferences and opportunistic, ‘r-selected’ life histories such as low age and size at maturity and low trophic level, and a concurrent increase in species with moderate to high temperature preferences and equilibrium, ‘K-selected’ life histories such as high size and age at maturity, few large offspring, and high parental care. Interestingly, we found significant temporal change in functional community structure at roughly half of all sites (36 of 79), whereas Auber et al. (2017b) found significant temporal change for only 13 sites when using

PRC with taxonomic data, highlighting that changes in community structure are better identified using ecological traits regardless of species identity.

The finding that the type of community change was similar across all sites indicates a regionally-consistent response across the entire EEC. Indeed, we found that changes in fish functional structure are strongly correlated with the Atlantic Multidecadal Oscillation and associated increases in sea surface temperature. While sea surface temperature has been progressively rising in the North Atlantic during the past few decades, the switch from a cool to a warm phase of the AMO lead to unusually-rapid warming (Moore et al., 2017; Ting et al.,

2009). This rapid warming likely amplified ongoing ‘tropicalisation’ throughout the Channel, causing an abrupt decrease of species sensitive to higher sea surface temperatures, and a concurrent increase in tolerant species (Cheung et al., 2013; Rijnsdorp et al., 2009; Vergés et

76 al., 2014). Indeed, we identified increases in species with high temperature preferences as contributive to the overall change in fish functional structure, and also identified correlations between AMO, SST, and temperature preference. While the North Atlantic Oscillation (NAO) was also correlated to changes in fish functional structure, the NAO is a regional index of atmospheric pressure that was associated with ocean warming in the late-1980s (Reid et al.,

2001), but has progressively declined in parallel to the AMO increase.

While AMO appeared the primary driver of changes in fish functional structure, RLQ analysis also identified substantial correlation between fishing mortality and functional group dynamics. However, both demersal and pelagic fishing pressure declined in parallel to decreasing fish abundances, and both fishing indices had identical relationships with the abundance of small pelagic fishes. Thus, fishing mortality was likely not a primary driver of changes in fish functional structure, as pelagic fishing mortality declined in parallel to a pronounced decrease in small pelagic fishes. The community shift being characterized by a rapid decrease in species with fast life history cycles also indicates an environmental response rather than progressive fishing impacts (Perry et al., 2005; Rijnsdorp et al., 2009). However, given that demersal fishing pressure was positively correlated with pelagic and planktivorous fishes and negatively correlated with demersal fishes, it is likely that historically high demersal fishing increased the relative abundance of pelagic fishes with rapid growth and generation times, rendering the community more susceptible to climate stress (Auber et al.,

2015; Molfese et al., 2014; Thurstan et al., 2010). Indeed the English Channel has been heavily exploited for decades, particularly following intense industrialization of commercial fisheries in the early to mid-1900s (Pauly et al., 2002). Long-term overfishing and fishing down the food web in the English Channel have progressively shifted the ecosystem from historical dominance by large demersal species such as cod and ling toward increased dominance by small pelagic and demersal species with higher fishing tolerances, and

77 commercially-untargeted species like catsharks (McHugh et al., 2011; Molfese et al., 2014).

Thus, while the rapid shift in fish functional structure appeared most strongly associated with climate-driven ocean warming, the long history of exploitation in the EEC clearly reinforced this shift by rendering the ecosystem susceptible to a climatic disturbance. Furthermore, the observed functional shift also reflects reduced contemporary fishing effort, as we observed an increase in larger and higher trophic level species through time (Pauly et al., 2002).

Altogether it appears that historical overfishing in combination with rapid environmental change induced a major shift in fish functional structure, as the EEC was dominated by species with environmentally-sensitive life history traits in the early 1990s, which were highly responsive to the shift in AMO.

While high temperature preference was identified as an influential trait, temperature preference is a ‘soft’ (i.e., easily measured but less informative) trait that serves as a proxy for

‘hard’ (i.e., informative but difficult to measure) physiological traits influencing species’ distributions and habitat preferences (Leps et al., 2006; Pakeman, 2011; Violle et al., 2007).

More interesting was the finding that increasing and decreasing species contrast strongly in life history traits related to offspring survival, population growth, and generation time.

Species with r-selected life histories follow type III survivorship curves, where many small offspring are produced and given little to no parental investment (Gadgil and Solbrig, 1972;

Pianka, 1970; Southwood et al., 1974). This strategy employs the trade-off that while survivorship is extremely low among progeny, larval dispersal and population turnover are high, allowing populations to quickly respond to unfavorable environmental conditions

(Gadgil and Solbrig, 1972; Pianka, 1970; Southwood et al., 1974). Alternatively, K-selected strategists make large energetic investments in few, well-developed offspring. While such a strategy limits dispersal and colonization abilities, progeny are strong competitors and have high individual fitness (Gadgil and Solbrig, 1972; Pianka, 1970; Southwood et al., 1974).

Beyond the classical distinction of r and K-selected strategies, recent work using ecological traits suggests a continuum of three life history strategies where r-selected species are considered ‘opportunistic’ and K-selected species can be split into ‘periodic’ and

‘equilibrium’ based on tradeoffs in fecundity, parental care, and offspring size (King and

McFarlane, 2003; Pecuchet et al., 2017; Winemiller and Rose, 1992). Opportunistic species are characterized by low size and trophic level and short lifespans, but with high fecundity

(King and McFarlane, 2003; Pecuchet et al., 2017; Winemiller and Rose, 1992). Both periodic and equilibrium species have larger size, higher trophic level, and longer lifespans, but periodic species have high fecundity and low parental care, whereas equilibrium species have low fecundity and high parental care (King and McFarlane, 2003; Pecuchet et al., 2017;

Winemiller and Rose, 1992). In the context of the three-strategy life history continuum, our results suggest that opportunistic species are the most responsive to rapid environmental change, notably warming, due to their shorter life cycles, while equilibrium species with few, well-developed offspring and longer life cycles, appear less responsive. Prior studies examining the relationship between life history strategies and climate change have shown that opportunistic species can be highly impacted by climate warming because short generation times enable rapid population responses (Hoffmann and Sgro, 2011; Jiguet et al., 2007;

Pearson et al., 2014). For example, Devictor et al. (2012) and Perry et al. (2005) documented faster and more pronounced distribution shifts among species with faster life cycles and smaller size in fishes, birds and butterflies, while Simpson et al. (2011) showed that smaller fish species responded faster to warming across European shelf seas.

While prior studies have documented rapid responses in opportunistic species, few examples have shown that such responses can drive major community shifts in entire species assemblages over large temporal and spatial scales (Devictor et al., 2012; Perry et al., 2005).

Here, our results suggest that temperature warming, amplified during the late 1990s by the

79 combination of man-made climate change and the AMO (Ting et al., 2009), led to an abrupt decrease in opportunistic species with fast life histories throughout the EEC, likely as a response to a rapidly changing environment (Bradshaw and Holzapfel, 2006; Pianka, 1970;

Stearns, 1989). It further appears that this decrease allowed equilibrium species to expand their populations under new environmental conditions. However, although opportunistic species are highly responsive to environmental change and can thus be heavily impacted over short time-scales, over evolutionary time opportunistic species should have higher capacity to adapt given their rapid evolutionary responses (Rijnsdorp et al., 2009).

Among potential explanations for the shift in functional structure, temperature rise likely affected larval and juvenile mortality rates through changes in dispersion and recruitment (Blaxter, 1991; Drinkwater et al., 2014; Young et al., 2018) or through match- mismatches with food sources (Kristiansen et al., 2011; McQueen and Marshall, 2017).

Additionally, as proposed by Auber et al. (2015), this community shift may have been influenced by density-dependent interactions such as predation and competition. Temperature rise is also known to drive species emigrations, and the community shift could have resulted from the rapid displacement of existing individuals (Day et al., 2018; Pinsky et al., 2013).

Finally, while opportunistic species can rapidly track environmental changes, allowing quick recovery when conditions return to normal, the community has not returned to the initial pre- shift state, suggesting environmental conditions are no longer favorable for the impacted species, inhibiting their recovery.

While we found interesting patterns linking ecological and life history traits to rapid environmental change, our study has several important limitations. As we examined changes in the abundance of functional groups, our results are influenced by both trait choice and categorization (Leps et al., 2006; Mcgill et al., 2006; Violle et al., 2007). Such methods remain subjective, and there is no universal approach for choosing traits or defining groups.

Binning species also leads to a necessary loss of trait information and can potentially combine species with different environmental responses. Our approach further cannot account for intra-specific trait variability, and thus cannot examine how changes in ontogeny or population demographics influence functional structure (Petchey and Gaston, 2006; Violle et al., 2007). We also used RLQ analysis to examine the potential drivers of temporal changes in fish functional structure; however RLQ is unable to identify statistical significance due to potential autocorrelation, and can only reveal associations among variables.

By using a trait-based approach, we were able to uncover the ecological characteristics linking species that drove a rapid shift in the EEC fish community. These findings increase our understanding of how organisms respond to environmental change and help anticipate how ecosystems might change in the future. Growing evidence shows it is essential to adopt a trait-based approach as it provides better understanding of biological mechanisms and because global change will have drastic impacts on biodiversity, which will be mediated through species’ functional characteristics.

2.6. Supplementary material

Supplemental Figure 1 | Temporal dynamics of the most contributive functional groups that decreased in abundance before and after the shift in functional structure

Supplemental Figure 2 | Temporal dynamics of the most contributive functional groups that increased in abundance before and after the shift in functional structure

3. Trait structure and redundancy determine sensitivity to disturbance

in marine fish communities3

3In press as McLean M., Auber A., Graham N.A.J., Houk P., Violle C., Thuiller W., Wilson

S.K., Mouillot D. 2019. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities. Global Change Biology

3.1. Preface

Chapter 2 revealed a major shift in fish functional-trait structure throughout the entire EEC in relation to historical fishing pressure and a warming phase of the AMO. Interestingly, this study also found that the type of community change (the functional traits that increased and decreased in abundance) was the same across all sites, but that the amplitude of community change was highly variable. While some sites displayed pronounced shifts in functional structure, others were more stable, and the amplitude of community change was spatially heterogeneous with no visible patterns with depth or distance from the coast. This finding inspired me to investigate whether the amplitude of community change across sites could be linked to biological structure itself, notably the initial composition of functional traits prior to the community shift or to the initial functional redundancy of communities. If biological structure was responsible for the amplitude of community change, it is possible that dominance by certain functional traits rendered communities more sensitive to shifts, or that communities with higher functional redundancy were more resistant. Thus, in Chapter 3, I developed an approach to define ‘functional sensitivity’ that could be linked directly to functional-trait composition and functional redundancy, and I used this approach to examine whether the amplitude of community change in the EEC was explained by initial trait structure or redundancy. Additionally, to examine the robustness of this approach and the results, I compared the findings of the EEC with a tropical ecosystem, the Seychelles Islands, to examine whether initial trait structure and redundancy would have similar effects on community shifts in two drastically different ecosystems.

3.2. Introduction

Global environmental changes are threatening the sustainability of ecosystem functions and services, with severe consequences for human livelihood and wellbeing (Bellard et al., 2012;

Cheung et al., 2016; Mora et al., 2015; Smith and Myers, 2018). It is therefore crucial to understand how variation in biodiversity may influence the sensitivity of communities and ecosystems to environmental change (Beaugrand et al., 2015; Heilpern et al., 2018; Nolan et al., 2018; Segan et al., 2016). Species diversity has been shown to sustain ecosystem productivity and stability under environmental disturbances (Isbell et al., 2015; Liu et al.,

2018; Mellin et al., 2014; Schneider et al., 2016). However, beyond species diversity, examining the diversity of organismal traits can provide a more mechanistic understanding of community dynamics via trait-environment relationships and through associated changes in ecosystem processes (Cadotte, 2017; Craven et al., 2018; Gross et al., 2017; Sakschewski et al., 2016).

Under the insurance (or redundancy) hypothesis, several species supporting similar key ecological roles, (i.e., sharing similar ecological traits), should buffer communities against the impacts of environmental change (Dee et al., 2016; Dı́az and Cabido, 2001;

Laliberte et al., 2010; Nash et al., 2016; Sanders et al., 2018). Limited experimental and observational evidence over small temporal and spatial scales support this theory. For instance, on coral reefs, key functions such as grazing and bio-erosion by large parrotfishes can maintain benthic community resilience following disturbances (Bozec et al., 2016;

Heenan et al., 2016; McLean et al., 2016), and redundancy within parrotfish groups may reinforce these functions (Burkepile and Hay, 2008). In plant communities, higher trait redundancy has been linked to higher community stability in experimental plots exposed to grazing (Pillar et al., 2013). However, long-term empirical evidence demonstrating the buffering effects of trait redundancy in ecological communities is lacking.

The insurance hypothesis additionally suggests that community dynamics depend not only on environmental variation but also on the initial biological structure of communities.

Indeed, the initial structure of communities can determine their successional trajectories under disturbance (Fukami et al., 2005), and two potential (non-mutually exclusive) biological mechanisms may explain differences in sensitivity among communities impacted by similar disturbances: 1) sensitive communities are more dominated by species with vulnerable traits

(to a given disturbance), and/or 2) sensitive communities have lower trait redundancy

(Walker, 1992; Williams et al., 2010). Thus communities’ initial trait structure at a baseline time period (i.e., pre-disturbance) could determine communities’ sensitivity or resistance to environmental disturbances (Barros et al., 2016). However, while limited evidence supports the insurance hypothesis, no study has examined whether key traits or initial trait redundancy can buffer communities against environmental change in natural systems across large temporal and spatial scales.

Here, using multidimensional spaces based on species’ ecological traits, we assessed whether community sensitivity was determined by initial trait structure or initial trait redundancy. Using long-term data from both temperate and tropical marine fish communities, we show that increased dominance by species with climatically-vulnerable traits rendered communities more susceptible to environmental change, while communities with higher trait redundancy were more resistant, demonstrating the potential buffering capacity of trait redundancy in diverse, natural systems.

3.3. Materials and Methods

3.3.1. Eastern English Channel: temperate marine fish communities

3.3.1.1. Disturbance

The fish communities of the Eastern English Channel (EEC) experienced a major shift in both taxonomic and trait structure in 1997 in response to an Atlantic-wide climate oscillation (The

Atlantic Multidecadal Oscillation) that led to rapid sea surface warming and oceanographic changes (Auber et al., 2015; Ting et al., 2009). While all sampling sites throughout the EEC were concurrently impacted by a basin-wide climate disturbance, the magnitude of community change was highly variable among sites with some sites experiencing very pronounced shifts and others remaining more stable (Auber et al., 2017b).

3.3.1.2. Fish community data

The fish community of the EEC (area VIId defined by the International Council for the

Exploration of the Sea, ICES) has been sampled every October since 1988 during the scientific monitoring campaign the Channel Ground Fish Survey (CGFS). Here, we focused on the study period of 1988 – 2011. The CGFS sampling scheme was spatially stratified by subdividing the EEC into 15’ longitude ×15’ latitude rectangles where at least one 30-min haul was made during daylight hours at an average speed of 3.5 knots. A high (3 m) vertical opening bottom trawl (GOV) with a 10-mm-stretched-mesh-size codend was used. After each haul, all captured fishes were identified and counted. Abundance indices at each sampling station were obtained from the ICES data portal and were standardized to numbers of individuals per km2 (ICES). Because weather sometimes prevents sampling, sites with three

89 or more consecutive years of missing data were removed, resulting in 79 total sampling sites.

Before analyses, abundance data were ln(Ax+1) transformed (where A*min(x)=2) (Auber et al., 2017b; Van den Brink et al., 2000) to reduce the influence of highly dominant species.

3.3.1.3. Traits

Following the definition of Violle et al. (2007), we examined community dynamics not only through ecological traits related to energy transfer, but also through traits related to environmental filtering and biological interactions like competition. More precisely, seven traits related to life history, habitat use, and trophic ecology were collected for 73 taxa (67 species, 6 identified to genera only). These included length and age at maturity, fecundity, offspring size, parental care, water column position, and trophic guild (Table S1). Trait data came from FishBase (Froese and Pauly, 2012), Engelhard et al. (2011), and Pecuchet et al.

(2017). Traits encompassing life history, trophic ecology, and habitat preferences were chosen as they have known influences over species responses to environmental changes and influences on ecosystem processes (Villéger et al., 2017). While some studies have recommended examining trait redundancy uniquely with ‘effect’ traits, there is much overlap between ‘response’ and ‘effect’ traits in marine fishes, and traits used in this study have known links to ecosystem functioning (Villéger et al., 2017). For example, body size and age at maturity can determine species responses through differences in population turnover and generation time (Perry et al., 2005), yet body size is a universal trait controlling mobility, feeding rate, and species-interactions, hence impacts on trophic networks and nutrient cycles

(Bellwood et al., 2019). Resource fluctuations and phenological mismatches can lead to shifts in trophic guild dominance (Hargeby et al., 1994; Thackeray et al., 2016), and trophic shifts can alter energy transfer and food web stability (Mumby et al., 2006). Water column position

90 has been linked to changes in distribution and abundance because pelagic fishes have greater capacity for range shifts (Rijnsdorp et al., 2009), but water column position also influences nutrient cycling and benthic-pelagic coupling (Griffiths et al., 2017).

3.3.1.4. Environmental factors

We examined environmental factors known to drive fish community dynamics in marine ecosystems, including depth, sea surface temperature (SST), and salinity. Although the majority of species in the English Channel are demersal, sea surface temperature was appropriate because the ecosystem is shallow (mean depth = 63 m) and well-mixed, and the majority of species have pelagic eggs and larvae, which are the most vulnerable life stages of marine fishes (Pepin, 1991). Depth was measured in-situ during community sampling, SST data came from the kriging-interpolated Ifremer AVHRR/Pathfinder database (Saulquin and

Gohin, 2010), and salinity data came from the NORWegian ECOlogical Model

(NORWECOM), a coupled 3D physical/bio-chemical model of environmental factors for the

North Sea and the English Channel (Skogen et al., 1995). Depth is a major driver of community structure in marine ecosystems as it influences light penetration, temperature and oxygen profiles, water column mixing, and habitat type. SST is a primary driver of species’ distributions and abundances globally, and SST warming can profoundly impact marine fish communities, which are highly responsive to changes in temperature (Simpson et al., 2011).

Salinity can influence community structure through physiological responses, impacts on larval success, and shifts between stenohaline and euryhaline species (Petereit et al., 2009; Sirot et al., 2015). Although chlorophyll-a can also determine community structure through bottom-up control and through larval success and recruitment (Beaugrand, 2004; Capuzzo et al., 2017), spatially-resolved data for chlorophyll-a were not available prior to 1998. Spatially-resolved

91 data for fishing pressure were also unavailable prior to 2000, however, the rapid shift in fish communities in the English Channel was driven primarily by climate and not by fishing

(Auber et al., 2015), and the objective of this study was to examine heterogeneity in community responses following the climatic disturbance.

3.3.2. Seychelles Islands: coral reef-fish communities

3.3.2.1. Disturbance

The Seychelles Islands experienced wide-spread coral mortality following severe coral bleaching during the 1998 El Niño that led to substantial changes in reef fish taxonomic and trait structure (Graham et al., 2015, 2006). The mass bleaching was severe across the entire inner Seychelles (Graham et al., 2015, 2006), and of the 21 sites surveyed, all but one site had losses in coral cover, with an average 65% loss across all sites. Aside from the bleaching, there were no other large-scale disturbances in the inner Seychelles during this timeframe, and other local stressors remained stable (Graham et al., 2006). While differential benthic trajectories following the mass bleaching have been linked to environmental and ecological conditions (Graham et al., 2015; Nash et al., 2016), variation in initial disturbance sensitivity has not yet been investigated.

3.3.2.2. Fish community data

Fish abundance data were collected at 21 sites around the Seychelles Islands using underwater visual census (UVC) in both 1994 (pre-disturbance) and 2005 (post-disturbance). At each site

16 individual 7-m radius (154 m2) stationary point counts were surveyed along the reef slope,

92 and the identity and density of diurnally active, non-cryptic reef fishes were recorded within each count (Graham et al., 2015, 2006). As with the EEC, abundance data were ln(Ax+1) transformed before analyses.

3.3.2.3. Traits

While intrinsic differences between the two study ecosystems lead to different trait choices, we attempted to maximize trait overlap between ecosystems. Life-history traits such as fecundity and offspring size are largely undescribed for coral reef fishes; however, length at maturity, age at maturity, and parental care were all available. Seven traits related to life history, habitat use, behavior, and trophic ecology were therefore collected for the 129 species sampled. These included length and age at maturity, parental care, water column position, trophic guild, mobility, and gregariousness (i.e., schooling behavior) (Table S2). Trait data were collected primarily from FishBase (Froese and Pauly, 2012) and previously published literature (Graham et al., 2015, 2011; Stuart-Smith et al., 2013; Wilson et al., 2008), while length and age at maturity estimates were derived from the R package FishLife (Thorson et al., 2017). These traits were chosen not only to maximize overlap between ecosystems, but also because they have been previously implicated in reef fish responses to environmental change and influences on ecosystem processes (Graham et al., 2011; Pratchett et al., 2008;

Stuart-Smith et al., 2013; Wilson et al., 2008). For example, body size and diet determine energy needs, predator-prey relationships, and trophic interactions, parental care determines habitat requirements and habitat modification, gregariousness influences predation vulnerability, nutrient cycling, and resource depletion, and mobility influences home range

(i.e., scale of ecological role) and nutrient transfer (Mouillot et al., 2014; Stuart-Smith et al.,

2013; Wilson et al., 2008).

3.3.2.4. Environmental factors

We examined known environmental drivers of reef-fish community structure, including management status (i.e., openly fished sites vs. marine reserves), depth, coral cover, and reef complexity. Fishing is a primary driver of reef fish community structure and can erode diversity and ecosystem functioning, and thus openly fished sites could be more sensitive to disturbance than marine reserves (Cinner et al., 2018; Houk et al., 2015). Depth controls coral-reef structure through light attenuation and wave exposure (Bridge et al., 2013) while coral cover and complexity can enhance habitat diversity and resource availability

(Richardson et al., 2017; Rogers et al., 2014). Within each stationary point count, the percent cover of live hard coral was quantified and the structural complexity of the reef was visually estimated (Graham et al., 2015, 2006). Structural complexity was assigned to one of the five categories: 0 = no vertical relief, 1 = low (<30 cm) and sparse relief, 2 = low but widespread relief, 3 = widespread moderately complex (30–60 cm) relief, 4 = widespread very complex

(60–100 cm), and 5 = exceptionally complex (>1 m) relief, which aligns with several other methods of assessing structural complexity on coral reefs (Wilson et al., 2007).

3.3.3. Quantifying sensitivity

3.3.3.1. Multidimensional trait space

We first generated a trait space for each ecosystem where species are arranged according to their trait values and distances between species reflect their trait similarity (Maire et al., 2015;

Mouillot et al., 2013b). We computed Gower dissimilarity matrices of the species by traits table for each ecosystem; Gower dissimilarity is well-adapted for examining traits as it can handle multiple data types (i.e., continuous and categorical) and missing values (Gower,

1971). Trait spaces were then built by ordinating the Gower matrices using principal coordinates analysis (PCoA) (Villéger et al., 2008). For each ecosystem, we kept the first four axes of trait space, which cumulatively explained nearly 80% of total variance in both cases.

Four axes have been shown to effectively capture community variation while maintaining initial Gower dissimilarity (Maire et al., 2015), and Mantel tests revealed that Euclidean distances between species in four-dimensional spaces were strongly correlated with initial

Gower distances (EEC: r = 0.96, p < 0.001; Seychelles: r = 0.96, p < 0.001). Including additional axes did not greatly improve these relationships (6 axes; EEC: r = 0.98; Seychelles: r = 0.98), demonstrating that the majority of variability in traits was accurately captured with four axes.

3.3.3.2. Sensitivity

Using the trait space, we quantified ‘sensitivity’ as the amount of change in trait structure following disturbance. Large shifts in trait structure indicate low resistance and thus high sensitivity, while small shifts indicate high resistance and low sensitivity. For a given ecosystem, we first calculated abundance-weighted community centroids in trait space for all sites in all time periods (Figure 1a). Within the trait space, the location of any given community in any given year is defined as the abundance-weighted centroid of all species in the community (Figure 1a). Thus, the movement of a community in the trait space can be used to quantify changes in trait structure through time. We therefore calculated sensitivity as the

Euclidean distance between a community’s position in trait space before and after disturbance

(Figure 1b). This quantifies the amount of distance each community moved in trait space following a disturbance, where sites with larger movements display higher sensitivity and sites with smaller movements display higher resistance. In the EEC, where an abrupt shift

95 occurred in the middle of a long time series, for each community, we calculated the distance between the average positon of all years before and all years after the shift (1997), while in the Seychelles, for each site, we calculated the distance between 1994 (pre-bleaching) and

2005 (post-bleaching).

Figure 1 | Conceptual diagrams showing the calculation of sensitivity according to changes in the abundance-weighted community position in trait space (a) following disturbance (b), along with potential links between i) sensitivity and initial trait structure (c) and ii) sensitivity and initial trait redundancy (TRed) (d). In (a) black circles represent species positions’ in trait space with sizes scaled by abundance and the white circle represents the abundance-weighted community centroid. Panel (b) shows sensitivity calculated as the distance moved in trait space following a disturbance. In (c) communities in the upper area of trait space have sensitive trait structures and exhibit large shifts (arrows), whereas communities in the lower area have resistant trait structures. In (d) communities with low trait redundancy exhibit large shifts (arrows), indicating high sensitivity, whereas communities with high trait redundancy exhibit resistance.

3.3.4. Trait redundancy

Redundancy quantifies the degree to which species in a community share similar ecological characteristics, i.e., whether ecological roles are supported by few or many species and 97 individuals. Multiple methods exist for calculating trait redundancy that are mostly based on deriving ecological groups or calculating distances between adjacent species in multidimensional space (Bruno et al., 2016; Micheli and Halpern, 2005; Mouillot et al., 2014;

Pillar et al., 2013; Ricotta et al., 2016). However, such indices require subjective choices such as the number of ecological groups or the cut-offs for nearest-neighbor calculations (e.g., nearest single neighbor vs. nearest five neighbors), and some indices only work with categorical traits (Bruno et al., 2016; Ricotta et al., 2016). Furthermore, most indices do not account for abundance or evenness, which can drastically impact trait redundancy as a higher number of individuals, not only species, supporting the same ecological role can provide greater buffering capacity against disturbances, and high evenness across species should lead to greater average redundancy than a community dominated by few species (D’agata et al.,

2016b). We therefore quantified trait redundancy following de Bello et al. (2007) and Ricotta et al. (2016) where trait redundancy is defined as the degree to which a community is

‘saturated’ with similar traits, and is calculated as the difference between taxonomic diversity

(Simpson’s index) and trait diversity (Rao’s quadratic entropy). In this way, communities with the same level of trait diversity but different levels of taxonomic diversity (or vice versa) will vary in trait redundancy, where a community with either more ecologically-similar species or higher species diversity will have higher redundancy (de Bello et al., 2007; Ricotta et al., 2016). Previous studies quantifying trait redundancy using this metric found significant relationships with community stability and environmental filtering, demonstrating the utility for examining relationships between redundancy and community dynamics while integrating species abundance distributions (Bruno et al., 2016; Kang et al., 2015; Pillar et al., 2013).

Additionally, this metric of trait redundancy is calculated at the community level using continuous data, and does not require defining ecological groups. Initial trait redundancy (i.e.,

98 pre-disturbance) was calculated for all fish communities in both ecosystems using the R package SYNCSA.

3.3.5. Generalized Linear Models (GLMs)

To consider the influences of environmental factors, for each site we calculated the change in each variable following disturbance (mean before vs mean after) rather than using temporally- averaged spatial variables, to avoid using static independent variables to predict dynamic dependent variables. Thus, changes in local environmental factors were used to predict changes in community trait structure. For instance, while the EEC was impacted by an

Atlantic-wide climate oscillation and associated ocean warming, local-scale variability in SST or salinity change could explain variability in community responses. Depth was the only factor included in all statistical models, as it is a permanent environmental condition (on ecological time scales). Therefore, in the EEC, we built generalized linear models (GLMs,

Gaussian distribution) testing the influences of i) the initial position of each fish community in trait space (PCoA 1 and PCoA 2 scores), ii) initial trait redundancy, iii) species richness, iv) depth, v) changes in local SST, and vi) changes in local salinity on community sensitivity. In the Seychelles, we used GLMs to test the influences of i) the initial position of each fish community in trait space (PCoA1 and PCoA2 scores), ii) initial trait redundancy, iii) species richness, iv) depth, v) percent change in coral cover, vi) percent change in reef structural complexity, and vii) management status on sensitivity. While sensitivity was calculated in four-dimensional space, only the first two PCoA axes were included in GLMs since they carry the majority of ecological-trait variation (>50%), and are thus sufficient to test the hypothesis that initial trait structure influences sensitivity. To identify the relative importance of independent variables, we used the dredge function from the R package MuMin, which

99 calculates Akaike weights for each variable by comparing Akaike Information Criteria across the set of models containing all possible combinations of variables (Arruda Almeida et al.,

2018; Barton, 2016). Next, to assess the robustness of the relationships between sensitivity and independent variables, we re-ran the analyses using all combinations of six traits out of seven. For each model repetition, we calculated the corresponding Akaike weights, and independent variables were ranked according to their mean Akaike weights across all models

(full model and all combinations of six traits). Finally, the full GLMs were checked for spatial-autocorrelation by testing model residuals with Moran’s I.

3.3.6. Null Models

To examine whether the relationships between sensitivity and i) initial trait structure, or ii) initial trait redundancy were significantly different than expected by chance, we built null models examining the slope of the linear regression between sensitivity and each metric, following Fukami et al. (2005). For each null model we randomly permutated species abundances (i.e., abundances were randomly shuffled between species), re-calculated abundance-weighted community centroids in trait space, re-calculated trait redundancy, and re-computed the corresponding linear models. This process was repeated 1000 times and the corresponding linear models were used to build null distributions of 1000 slopes. The actual observed slopes between sensitivity and i) initial position in trait space, and ii) initial trait redundancy were then compared to the resulting null distributions.

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3.4. Results

3.4.1. Climatic disturbance in a temperate marine fish community

In the EEC, the first two axes of trait space captured 52% of overall variation in fish trait structure across the 73 taxa (Figure 2a), while the third and fourth axes explained an additional 24%. The first axis of trait space was primarily characterized by differences between large-bodied, long-lived species with large offspring (lower values) vs. small-bodied, short-lived pelagic species and detritivores (higher values), while the second axis was characterized by differences between carcinophages and benthopiscivores with high parental care and large offspring (higher values) vs. pelagic species, planktivores, and detritivores with low parental care and small offspring (lower values) (Figure 2a; Figure S2). We found that the distance each community moved across the trait space following the disturbance was significantly correlated to the initial position of each community along the first and second axes of space, as communities with lower PCoA 1 and PCoA 2 values experienced higher changes in trait structure following the disturbance (PCoA 1: r = -0.25, p < 0.05; PCoA 2: r =

-0.27, p = 0.01; Figure 2a,b; Figure 3a; Figure S1). We found that distance moved was also significantly and negatively correlated to initial trait redundancy: fish communities with higher trait redundancy were more resistant and had less pronounced shifts (r = -0.50, p <

0.0001; Figure 3b). GLMs then ranked PCoA 2 position, trait redundancy, and local salinity changes as the most important variables predicting the distance moved by each community across the trait space (Top 3 variables, Table 1), with secondary contributions by local temperature changes, depth, PCoA 1 position, and species richness. No spatial autocorrelation was found in the model residuals (Moran’s I, p = 0.41), indicating the results were robust to spatial effects and that we did not omit major factors in the models.

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Figure 2 | Trait space and sensitivity of fish communities in the Eastern English Channel (EEC) and Seychelles Islands. a) Trait structure of the overall EEC fish community. b) Sensitivity of fish communities in the EEC defined by the distance moved (i.e., amount of change) in trait space following disturbance; larger circles = higher movement and therefore higher sensitivity. c) Trait structure of the overall Seychelles fish community. d) Sensitivity of fish communities in the Seychelles defined by the distance moved in trait space following disturbance.

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Figure 3 | Partial relationships from Generalized Linear Models (GLMs) between sensitivity and the most important explanatory factors in the Eastern English Channel (EEC) and Seychelles Islands. Relationships between sensitivity and i) initial position on PCoA axis 2 of trait space (a), ii) initial trait redundancy (b), and iii) local salinity changes (c) in the EEC. Relationships between sensitivity and i) initial position on PCoA axis 1 of trait space (d), ii) initial trait redundancy (e), and iii) coral cover change (f) in the Seychelles. Relationships were plotted using the inverse link function from GLMs via the R package Visreg.

Table 1 | Results of Generalized Linear Models using Akaike weights to assess and rank the importance of independent variables in determining sensitivity. Akaike weight means and standard deviations were derived across all models (full model of 7 traits and all combinations of 6 out of 7 traits).

Eastern English Channel Seychelles Islands Factors AIC Weight Factors AIC Weight Mean ± SD Mean ± SD Trait redundancy 0.99 ± 0.01 Initial PCoA 1 position 0.56 ± 0.21 Initial PCoA 2 position 0.98 ± 0.03 Trait redundancy 0.44 ± 0.21 ∆ Salinity 0.84 ± 0.13 ∆ Coral cover (%) 0.33 ± 0.12 ∆ Sea surface temperature 0.40 ± 0.10 Depth 0.32 ± 0.10 Depth 0.36 ± 0.07 Initial PCoA 2 position 0.24 ± 0.14 Initial PCoA 1 position 0.28 ± 0.07 Species richness 0.21 ± 0.04 Species richness 0.24 ± 0.01 ∆ Reef complexity (%) 0.19 ± 0.06 Management status 0.18 ± 0.02

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Null models indicated that the slope of the relationship between sensitivity and initial

PCoA 2 position was larger than expected by chance, albeit not significantly, as the observed slope was smaller than the 95% most extreme expected values (Figure 4a). However, the slope of the relationship between sensitivity and initial trait redundancy was significantly larger than expected by chance, as the observed slope was greater than 95% of slopes in the null distribution (Figure 4b).

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Figure 4 | Results of null models comparing the observed slopes of the relationships between sensitivity and i) initial PCoA 2 position, and ii) initial trait redundancy in the Eastern English Channel (a, b) and between sensitivity and i) initial PCoA 1 position, and ii) initial trait redundancy in the Seychelles Islands (c, d). Solid black lines indicate the mean slope of the null models, while dashed lines and dotted lines indicate the 95th and 99th percentiles of the null models, respectively.

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3.4.2. Reef-fish community responses to coral bleaching

In the Seychelles, the first two axes of trait space captured 52% of overall variation in fish trait structure for the 129 species (Figure 2c), while the third and fourth axes explained an additional 22%. The first axis of trait space was primarily characterized by differences between species with large size and age at maturity, high mobility, piscivorous diets, and solitary behavior (lower values) vs. small, fast-growing planktivores and corallivores with high parental care (i.e., hide and guard eggs within coral habitats), and schooling behavior

(higher values), while the second axis was characterized by differences between invertivore feeders with solitary behavior and closer association the benthos (lower values) vs. piscivores and microphages with higher position in the water column (higher values) (Figure 2b; Figure

S3). We found that the distance moved by each community between the two time periods

(1994 and 2005) was significantly correlated to the initial position of each community along

PCoA axis 1, as communities with higher PCoA 1 scores had larger changes in trait structure

(r = 0.56, p < 0.01; Figure 2d; Figure 3d; Figure S1). We next found that the distance moved by each community was also significantly and negatively correlated to initial trait redundancy, as communities with higher trait redundancy were less sensitive and more resistant to changes in trait structure (r = -0.59, p < 0.01; Figure 3e). GLMs then ranked trait redundancy, PCoA 1 position, and coral cover change as the most important independent variables predicting the distance moved by each community in trait space (Top 3 variables, Table 1) with secondary contributions by depth, PCoA 2 position, species richness, reef complexity change, and management status. Finally, Moran’s I revealed that there was no spatial autocorrelation in the model residuals (p = 0.49).

Null models indicated that the slope of the relationship between sensitivity and both i) initial PCoA 1 position and ii) initial trait redundancy were significantly larger than expected

106 by chance, as the observed slopes were greater than 95% and 99% of slopes in the null distributions, respectively (Figure 4c,d).

3.5. Discussion

Despite wide belief that trait structure and redundancy can determine community sensitivity to disturbance, little evidence exists from natural systems over large temporal and spatial scales. Additionally, while functional ecology has operated on the presumed buffering capacity of trait redundancy, few studies have quantitatively demonstrated links between trait redundancy and community sensitivity outside of controlled experiments. Here, we present one of first studies using long-term data in two distinct ecosystems showing that trait structure and redundancy may determine community sensitivity to disturbance and that trait redundancy may buffer communities against environmental change. Past experimental and small-scale observational studies have shown that higher levels of trait redundancy can maintain community stability in the face of environmental change (Allison and Martiny,

2008; Loreau, 2004; Rosenfeld, 2002; Wohl et al., 2004); however, here we used datasets spanning nearly 25 years to examine changes in large, natural ecosystems in both temperate and tropical environments. Our findings support long-standing theory that higher diversity

(i.e., richness and abundance) supporting ecological roles can generate greater community stability, reducing sensitivity to climatic disturbances (Elmqvist et al., 2003; Rosenfeld, 2002;

Walker, 1992).

As disturbances in both ecosystems were related to climate warming, our results provide insight for understanding biodiversity responses to climate change. Not only were communities with lower trait redundancy more sensitive to climatic disturbances, but dominance by certain trait values rendered communities particularly sensitive to disturbance.

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For instance, in the EEC, dominance by small, r-selected pelagics led to greater shifts in trait structure through time as small pelagic species are highly responsive to environmental change, particularly warming and changes in oceanographic processes (Alheit et al., 2014;

Lindegren et al., 2013; Rijnsdorp et al., 2009). Pelagic fishes are also highly mobile and can quickly migrate or shift distribution, whereas demersal and reef-associated taxa may have greater site fidelity and less response capacity (Alheit et al., 2014; Lindegren et al., 2013;

Rijnsdorp et al., 2009). In the Seychelles, communities more dominated by small, gregarious corallivores and planktivores were more impacted by large-scale coral mortality, which is consistent with studies showing that small-bodied fishes feeding on and around corals typically decline following disturbance (Graham et al., 2007; Pratchett et al., 2008;

Richardson et al., 2018). While the climatic vulnerability of small pelagics and small corallivores is well-documented, our approach demonstrates that initial trait structure can predict community sensitivity to climatic disturbance, which is likely applicable across ecosystems and taxa. Our results therefore highlight the need to identify key trait-environment relationships in marine ecosystems worldwide to anticipate how climate warming might impact current and future communities through changes in trait structure.

While certain trait structures rendered communities more sensitive to disturbance, communities with higher trait redundancy were more resistant. Trait redundancy may buffer communities through multiple underlying mechanisms. According to the portfolio effect, if community structure is supported by many ecologically-similar species and these species exhibit asynchronous responses to disturbance, the lesser-impacted species should maintain overall community structure (Loreau and de Mazancourt, 2013; Oliver et al., 2015; Yachi and

Loreau, 1999). High evenness, where each ecological role is supported by similar species richness and abundance, should also increase community resistance, whereas communities over-represented by few, dominant species may leave some ecological roles with low

108 redundancy and weak insurance (Flöder and Hillebrand, 2012; Oliver et al., 2015; Wittebolle et al., 2009). For instance, if evenness is low and dominant species are sensitive to disturbance, communities will be highly impacted, but if evenness is high, resistant species may maintain community structure (Flöder and Hillebrand, 2012; Wittebolle et al., 2009).

Additionally, redundancy in species interactions may buffer communities by increasing food web stability. For example, loss of key trophic interactions can destabilize community structure (Kuiper et al., 2015), and trophic redundancy has been shown to reduce vulnerability to cascades (Sanders et al., 2018). In the EEC, trait diversity was relatively similar across communities, while taxonomic diversity was higher along the coasts, leading to greater trait redundancy, as ecological roles were supported by more species and greater abundances

(Figure S6). Communities exhibiting the greatest shifts in trait structure had low species richness and abundance, meaning some ecological roles had only limited support. Although pelagic fishes were highly impacted throughout the ecosystem, resistant communities had higher richness and abundance of pelagics, which appeared to buffer them against changes in trait structure. For example, horse mackerel exhibited exceptional declines (Auber et al.,

2017b, 2015) and communities mainly dominated by horse mackerel suffered major shifts from pelagic to demersal structure, whereas communities with large abundances of not only horse mackerel but also sprat, herring, and Atlantic mackerel (ecologically-similar species) had greater capacity to maintain initial structure (Figure 5). It therefore appears that the combination of higher richness and evenness supporting similar levels of trait diversity in the

EEC lead to higher resistance and lower sensitivity.

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Figure 5 | Relationship between trait redundancy and sensitivity in two fish communities from the Eastern English Channel (chosen to highlight low and high redundancy examples). In panels a and c, circles represent species, and circle sizes are scaled by initial (i.e., pre-disturbance) abundances. In panels b and d, circle sizes are scaled by species’ abundance changes and colors indicate whether abundances increased or decreased. The community with low trait redundancy (a, b) had only one dominant species supporting the pelagic ecological role (bottom-right corner), while the community with high trait redundancy (c, d) had multiple abundant species supporting the pelagic role. Both sites suffered major declines in one pelagic species and minor increases in demersal species (b, d). However the site with low trait redundancy underwent a major shift from pelagic to demersal community structure (b), whereas the site with high trait redundancy was resistant to change (d), as the remaining abundant pelagic fishes maintained the ecological role.

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It is also important to note that higher trait diversity does not necessarily correspond to higher overall trait redundancy, as ecologically-simple communities can have high redundancy (Casatti et al., 2015; Fonseca and Ganade, 2001; Mouillot et al., 2014). In the

Seychelles, the most impacted communities were actually those with the highest trait diversity

– species in these communities varied greatly in trait composition. However, taxonomic diversity was more even among communities, indicating that, although impacted communities had high trait diversity, individual ecological roles were supported by fewer species and individuals (Figure S7). Thus, higher trait redundancy in resistant communities resulted from similar levels of richness and abundance being shared among fewer ecological roles.

Additionally, it is important to note that the impact of ecological disturbances and the buffering capacity of trait redundancy depend on the type of disturbance and on which ecological roles are affected. For instance, the least impacted communities in the Seychelles were intuitively those with lower abundances of small corallivores. Thus, while the ecological roles supported by these species, e.g., shaping coral diversity (Cole et al., 2008), were most impacted following disturbance, these roles were already low in unaffected communities.

Hence, unaffected communities had high trait redundancy within ecological roles (e.g., large- bodied invertivores) that were not heavily impacted by disturbance. Yet, if the impacted ecological roles had been highly redundant, sensitive communities may have been more resistant. For instance, communities more characterized by small-bodied, site-attached corallivores and planktivores had higher sensitivity. However, corallivores and planktivores accounted for only 12% of all species and only 14% of trophic guild abundance on average, whereas invertivores accounted for 43% of species and 46% of trophic guild abundance.

Thus, if small-bodied corallivores and planktivores had greater initial redundancy, sensitive communities may have been buffered against disturbance, particularly through additional species with asynchronous environmental responses.

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While our findings support the conclusion that trait redundancy may buffer communities against disturbance, alternative explanations are possible. For example, trait structure and redundancy are intrinsically correlated with environmental conditions, and we found that sensitivity was also related to environmental change in both ecosystems. In the

EEC, salinity changes may have contributed to declines in species with small pelagic eggs and larvae, as salinity influences egg buoyancy and survival (Nissling et al., 2017; Petereit et al.,

2009; Sundby and Kristiansen, 2015). However, salinity changes in the EEC were much lower than those normally reported to impact fish community structure (Petereit et al., 2009;

Sirot et al., 2015). In the Seychelles, we found that fish communities experiencing greater losses of coral cover and at shallower depths had higher sensitivity and lower resistance to disturbance, which is in line with previous studies showing that coral-loss reorganizes reef fish communities (Richardson et al., 2018) and that shallower communities are more sensitive to disturbances like bleaching (Bridge et al., 2013). If sensitivity in the Seychelles was driven by greater environmental impacts, the apparent buffering capacity of trait redundancy could be an artifact of lower redundancy on reefs with greater coral loss. However, it is likely that both initial biological structure and local environmental changes are driving sensitivity and their influences may be synergistic.

As with all trait-based approaches, the choice and number of traits may have important impacts on the patterns of trait diversity and redundancy (Leps et al., 2006; Violle et al., 2007;

Violle and Jiang, 2009). For example, communities may have little redundancy along one niche axis, but high redundancy along another, and contrasting trends in the two axes could blur redundancy patterns (Micheli and Halpern, 2005; Spasojevic and Suding Katharine N.,

2012). However, multiple traits are needed to capture nuances among species, as combinations of traits (e.g., habitat type and life history) may act synergistically, leading to higher or lower disturbance sensitivity (Mouillot et al., 2013b; Villéger et al., 2010, 2017).

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However, re-running our analyses with all combinations of six out of seven traits did not substantially modify results, as trait structure and redundancy emerged as the most important explanatory factors of sensitivity (Figure S4, Figure S5).

While we examined the influence of biological structure on community sensitivity to disturbance, future studies should also examine the environmental drivers of trait redundancy itself. Linking gradients in environmental condition or human stressors to trait redundancy could be particularly informative for resource management. If human stressors are reducing trait redundancy, management strategies could be adapted to enhance redundancy for increased resilience against future disturbances. In the Seychelles Islands, management status was not linked to environmental sensitivity, yet other natural and human drivers such as productivity or watershed pollution could have shaped initial trait structure and redundancy.

While we were unable to quantify the influence of historical fishing pressure on sensitivity in the English Channel, past studies have concluded that overfishing may have rendered fish communities more sensitive to climatic changes by removing large demersal predators

(Molfese et al., 2014). Thus, identifying the natural and anthropogenic drivers of ecological- trait structure and redundancy should be prioritized in future studies and resilience assessments. Future studies should also attempt to identify thresholds of ecological trait and trait redundancy values to reveal tipping points in ecosystem stability that could be used as tangible management targets for maintaining resilience. As biodiversity loss threatens ecosystem functioning worldwide, identifying and conserving the mechanisms of community stability will be critical to maintaining the diversity needed to support ecosystem services.

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3.6. Supplementary material

Supplemental Table 1 | Ecological traits used to characterize fish community trait structure in the Eastern English Channel.

Ecological Trait Category Type Units

Length at maturity Life history Numeric Total length (cm)

Age at maturity Life history Numeric Years

Parental care Life history Ordered factor Pelagic egg, benthic egg, clutch hider, clutch guarder, live bearer

Fecundity Life history Numeric Number of offspring

Offspring size Life history Numeric Total length or diameter (mm)

Trophic guild Trophic ecology Factor Benthivore, benthopiscivore, carcinophage, detritivore, piscivore, planktivore

Water column position Habitat use Factor Bathydemersal, benthopelagic, demersal, mesopelagic, pelagic, reef-associated

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Supplemental Table 2 | Ecological traits used to characterize fish community trait structure in the Seychelles Islands.

Ecological Trait Category Type Units

Length at maturity Life history numeric Total length (cm)

Age at maturity Life history numeric Years

Parental care Life history Ordered factor None, clutch guarder Trophic guild Trophic ecology Factor Corallivore, herbivore/detritivore, invertivore, microphage, piscivore, planktivore

Water column position Habitat use Factor Benthopelagic, demersal

Schooling Behavior Ordered factor Solitary, pairing, schooling

Mobility Behavior Ordered factor Stationary, low mobility, high mobility

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Supplemental Figure 1 | Trait space showing community movement following disturbance in both ecosystems. a) Convex hull of trait space in the Eastern English Channel (EEC) with the initial position of all communities before disturbance. b) Arrows show the movement of each community in trait space following disturbance. c) Initial position of each community with the size of each circle scaled by the distance moved in trait space, i.e. sensitivity. a) Convex hull of trait space in the Seychelles Islands with the initial position of all communities before disturbance. b) Arrows show the movement of each community in trait space following disturbance. c) Initial position of each community with the size of each circle scaled by the distance moved in trait space, i.e. sensitivity.

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Supplemental Figure 2 | Pearson correlations between initial (pre-disturbance) community- weighted mean trait values and initial position along the first (PCoA 1) (a) and second (PCoA 2) (b) axes of trait space for fish communities in the Eastern English Channel. The strong negative correlation between piscivores and PCoA 2 is heavily influenced by one pelagic species, Trachurus trachrus, which feeds on both zooplankton and small fishes depending on ontogeny and resource availability.

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Supplemental Figure 3 | Pearson correlations between initial (pre-disturbance) community- weighted mean trait values and initial position along the first (PCoA 1) (a) and second (PCoA 2) (b) axes of trait space for fish communities in the Seychelles Islands.

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Supplemental Figure 4 | Sensitivity plots for the Eastern English Channel showing the results of Generalized Linear Models (GLMs) using all combinations of six out of seven traits to examine the relationships between community sensitivity and i) initial PCoA 2 position, and ii) initial trait redundancy. Relationships were plotted using the inverse link function from GLMs via the R package Visreg.

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Supplemental Figure 5 | Sensitivity plots for the Seychelles Islands showing the results of Generalized Linear Models (GLMs) using all combinations of six out of seven traits to examine the relationships between community sensitivity and i) initial PCoA 1 position, and ii) initial trait redundancy. Relationships were plotted using the inverse link function from GLMs via the R package Visreg.

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Supplemental Figure 6 | Maps showing the spatial heterogeneity of initial (i.e., pre- disturbance) total abundance, species richness, trait redundancy, and sensitivity in the Eastern English Channel. All values are normalized between 0 and 1.

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Supplemental Figure 7 | Maps showing the spatial heterogeneity of initial (i.e., pre- disturbance) total abundance, species richness, trait redundancy, and sensitivity in the Seychelles Islands. All values are normalized between 0 and 1.

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4. Functional reorganization of marine fish nurseries under climate

warming4

6Published as McLean M., Mouillot D., Goascoa N., Schlaich I., Auber A. 2018. Functional reorganization of marine fish nurseries under climate warming. Global Change Biology

25:660-674

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4.1. Preface

Chapter 2 and 3 documented a major shift in fish functional structure in the EEC characterized by decreases in pelagic species and species with rapid life-history cycles.

Because the main driver of changes in fish functional structure in the EEC was an Atlantic- wide climate oscillation known to impact biodiversity across multiple spatial scales, an important question emerged: were similar shifts in fish functional structure observed in estuary ecosystems within the EEC that act as nursery grounds for important commercial fishery species? If so, not only would this indicate an important link in functional dynamics between the EEC and its nurseries, but also reveal implications for fish communities in the

EEC – because fish nurseries are important for maintaining fish populations in larger ecosystems, functional shifts in nurseries could inhibit population recovery in the EEC.

Furthermore, if consistent functional dynamics were found among the EEC and its nurseries, it would reinforce the conclusions about fish community responses to large-scale climate variation. Therefore, in Chapter 4, I examined the long-term functional dynamics of fish communities in the Bay of Somme, an estuary of the EEC that provides an important fish nursery for commercial species such as sole, plaice, sprat, and herring, and discussed the potential links between the Bay of Somme and the EEC.

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4.2. Introduction

Climate change impacts are increasingly prevalent in marine ecosystems worldwide, with documented shifts in species’ distributions and community composition driven by increasing temperatures, reduced oxygen, and ocean acidification (Givan et al., 2018; Nash et al., 2017;

Shaffer et al., 2009; Sunday et al., 2016). Such climate change impacts are further expected to increase in frequency and severity under current projections of carbon dioxide emissions and ongoing warming (Comte and Olden, 2017; Henson et al., 2017). However, as recent climate change has been exceptionally rapid (Bradshaw and Holzapfel, 2006; Burrows et al., 2011;

Loarie et al., 2009; Schofield et al., 2010) with more frequent extreme events (Diffenbaugh et al., 2017; Frölicher et al., 2018; Hughes et al., 2018), scientists are only beginning to understand the many different ways global warming can impact ecosystems and communities, and latent impacts have likely gone undocumented.

As most studies have examined the impacts of climate warming on ecological communities through species-based approaches (Poloczanska et al., 2013; Wiens, 2016), it remains relatively unknown how increasing temperatures might alter communities’ functional structures (i.e., the composition of species’ functional traits) (Frainer et al., 2017). Functional- trait approaches are particularly valuable for understanding climate change impacts as environmental change affects organisms through their functional characteristics (e.g., diet, habitat preference, life-history), which have known links to ecosystem processes (Buisson et al., 2013; Gallagher et al., 2013; Villéger et al., 2017). Understanding how ecosystem functioning could change under future warming is critical to maintaining ecosystem services, and therefore studies assessing the functional responses of fish communities to climate change are urgently needed.

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Marine fish nurseries are critical habitats for maintaining ecosystem diversity and productivity because they act as population sources for larger ecosystems via recruitment and replenishment (Beck et al., 2001; Dahlgren et al., 2006; Gillanders et al., 2003; Vasconcelos et al., 2010). For example, mangrove and lagoon nurseries make substantial contributions to adult populations in nearby ecosystems, as they provide productive structural habitats suitable for rapid growth and maturation (Nagelkerken et al., 2017; Tournois et al., 2017). Thus, changes in fish nurseries could have major impacts in larger, adjacent ecosystems.

Furthermore, nurseries could be particularly sensitive to climate warming as they are depth- limited and likely to warm quickly with limited inertia to temperature fluctuations (Dulvy et al., 2008; Rutterford et al., 2015). However, the impacts of climate warming on fish functional structure and ecosystem functioning in marine nurseries have been surprisingly understudied, and few studies have disentangled the impacts of climatic and human pressures in such ecosystems (Chevillot et al., 2016; Nicolas et al., 2010; Petitgas et al., 2013; Sloterdijk et al., 2017).

In the present study, we assessed the influences of environmental drivers and fishing on the spatial and temporal dynamics of fish functional structure in the Bay of Somme, an estuary in northwestern France that provides a nursery habitat for the Eastern English

Channel. By examining communities in the Bay of Somme through a functional-trait approach, we found that the nursery was inherently susceptible to climate warming, as communities were initially dominated by fishes with environmentally-sensitive life-history traits. Consequently, fish functional structure changed significantly over time in parallel to exceptional sea surface warming, which was likely exacerbated by historical overfishing. Our study fills a critical knowledge gap by highlighting how climate warming can impact fish nurseries over long time periods, providing valuable information for fisheries management and conservation planning.

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4.3. Materials and methods

4.3.1. Study site and fish community sampling

The Bay of Somme is an estuary of the Eastern English Channel (EEC), which provides a nursery for several commercially-important marine fish species such as plaice, sole, and herring (Auber et al., 2017a; Le Pape, 2005; Rybarczyk et al., 2003). The Bay of Somme is the second largest (of five) major fish nurseries in the EEC and is representative of regional estuary habitats (Le Pape, 2005). The bay forms the mouth of the Somme River, but is primarily a marine ecosystem with an estuary habitat limited to the river channel (Auber et al.,

2017a; Rybarczyk et al., 2003). The bay has an intertidal surface of more than 50 km2 with a maximal tidal range of 11 m and an average tidal volume of 200 × 106 m3 (Rybarczyk et al.,

2003). The main river input comes from the Somme River, which has an estimated average runoff of 33.6 m3s-1 (Rybarczyk et al., 2003). Fish communities in the bay have been surveyed in late summer each year since 1987 during the NOURSOM fish monitoring campaign, and data from 1987 to 2012 were included in this study. Fish survey methods were defined according to three depth zones: inner bay from the shoreline to the mouth of the bay, 0 – 5 m deep in the outer bay, and 0 – 11 m deep in the outer bay (Figure 1). The monitoring campaign aimed to complete 44 hauls each year (based on feasibility): 16 hauls in the inner bay using a beam trawl with a 2-m horizontal opening (CP2), and 28 hauls in each zone of the outer bay using a beam trawl with a 3-m horizontal opening (CP3). The vertical opening was

0.31 m for CP2 trawls and 0.42 m for CP3 trawls, while the stretched mesh size was 20 mm for both. All CP2 hauls lasted 7 minutes while CP3 hauls lasted 15 minutes. Trawls were conducted during daylight hours at an average speed of 2.5 knots. Within each haul, fishes were identified and counted and all abundance values were standardized to numbers of individuals per km2.

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4.3.2. Environmental factors

We examined both local environmental factors as well as Atlantic-wide climate oscillations known to alter environmental conditions across multiple spatial scales (Dickson, 2000;

Edwards et al., 2013). Local environmental factors included depth, sea surface temperature

(SST), salinity, chlorophyll-a, suspended sediment, and bed shear stress (i.e., water velocity along the bottom), all of which have well-documented influences on the structure and distribution of marine fish communities (Beaugrand, 2004; Bœuf and Payan, 2001; Hermant et al., 2010; Kjelland et al., 2015). Depth and salinity were measured in-situ during the

NOURSOM survey. Spatially-resolved data for SST, chlorophyll-a and suspended sediment were derived from satellite data for the period 1998 – 2012 (Gohin, 2011) and bed shear stress was taken from a 3D hydrodynamic model developed by Aldridge and Davies (1993). These data were used for spatial analyses only. For temporal analyses, mean-annual SST for the entire bay was derived as the average of all sources available for the entire time series. These included the HadISST database (Rayner et al., 2003), the kriging-interpolated Ifremer

AVHRR/Pathfinder database (Saulquin and Gohin, 2010), in-situ measurements recorded during the ecological monitoring campaign of the Penly Nuclear Power Plant (NPP; mean of

April, July, and September) (Cochard, 2002), and in-situ measurements taken during Ifremer plankton monitoring campaigns (mean of May and June) (Ifremer unpublished data). Mean- annual salinity was derived as the average of in-situ measurements taken during the Penly

NPP and Ifremer plankton monitoring campaigns. Both mean-annual chlorophyll-a and suspended sediment were measured in-situ during the Penly NPP environmental monitoring campaign (Cochard, 2002).

The North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) are naturally-occurring, basin-wide climate oscillations that influence sea surface temperatures and oceanographic processes across the North Atlantic Ocean (Dickson, 2000;

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Edwards et al., 2013). Specifically, the NAO is an alteration of atmospheric mass over the

North Atlantic, which is characterized by warm, wet conditions during positive phases and cold, dry conditions during negative phases (Dickson, 2000; Hurrell et al., 2003), while the

AMO is a 60 – 80-year cycle of North Atlantic sea surface temperatures (Enfield et al., 2001).

Such basin-wide climate oscillations have been shown to impact ecosystem processes and biodiversity at local spatial scales, including estuaries (Hallett et al., 2004; Tolan, 2007).

Annual values of both the NAO and AMO (unsmoothed) came from the National

Oceanographic and Atmospheric Administration (NOAA, US).

4.3.3. Fishing pressure

The Bay of Somme has no local commercial fishery (outside of a small shrimp harvest), and there have been nearly no commercial landings in the bay in the last 40 years (Dreves et al.,

2010). Although there is no local fishery, as the bay provides a nursery for the EEC, fishing in the EEC has a potential impact on fish communities in the bay. Fishing pressure indices at the community and gear-type level were unavailable for the EEC prior to 2000. While fishing mortality indices are available, they are limited to few quantitatively-assessed commercial species. However, long-term fisheries landings data in the EEC were available for 34 (of 52; see fish functional structure below) Bay of Somme taxa for 1987 – 2010. Annual fisheries landings (tons per year) for each species for the entire EEC (International Council for

Exploitation of the Sea (ICES) area VIId) were therefore extracted from the ICES catch statistics database (ICES).

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4.3.4. Fish functional structure

We characterized fish functional structure using nine traits related to life history, habitat use, and trophic ecology (Table 1). Functional trait data came primarily from FishBase (Froese and Pauly, 2012), Engelhard et al. (2011), and Pecuchet et al. (2017). A literature search was also conducted to find missing trait values not available in the above sources. We compiled functional trait data for all observed species and taxonomic groups for which data were available, resulting in 52 taxa (48 species, 4 identified to genus level only). The final trait database had only 5 missing values out of 468 possible (9 traits x 52 taxa). Temperature preference was calculated as the median sea surface temperature of a species’ occurrences across its global range of observations for which data were available. Occurrence data came from FishBase, the Global Biodiversity Information Facility https://www.gbif.org/, and the

Ocean Biogeographic Information System http://iobis.org/.

Table 1 | Functional traits used to characterize fish functional structure.

Functional Trait Category Type Units Length at maturity Life history Numeric Total length (cm)

Age at maturity Life history Numeric Years

Parental care Life history Ordered 1 = pelagic egg, 2 = benthic egg, 3 = clutch hider, 4 = factor clutch guarder, 5 = live bearer

Fecundity Life history Numeric Number of offspring per female

Offspring size Life history Numeric Total length or diameter (cm)

Trophic guild Trophic Factor Benthivore, benthopiscivore, detritivore, piscivore, ecology planktivore

Trophic level Trophic Numeric Level (unit-less) ecology Water column Habitat use Factor Benthopelagic, demersal, epipelagic, pelagic, reef- position associated

Temperature Habitat use Numeric Degrees Celsius preference

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We first generated a table of community-weighted mean (CWM) trait values using log10(x+1) transformed abundances and standardized traits for all sampling locations and years for the entire Bay of Somme. We then ran preliminary analyses to determine the relative importance of spatial and temporal dynamics in explaining overall variation in fish functional structure. For these preliminary analyses, we aggregated sampling locations to fifteen spatial sites by rounding the latitude and longitude of each sampling location to the nearest tenth of a degree, which created an evenly-spaced spatial grid. Aggregating sampling locations to evenly-spaced sites was necessary to examine spatial variation as sampling locations were never re-sampled, and each sampling location corresponded to one row in the dataset. To determine the relative importance of space and time we used redundancy analyses (RDA) with i) aggregated spatial sites and ii) years as single explanatory factors. RDA is a method for examining the variation in a set of response variables (i.e., fish communities) using a set of explanatory variables, and is conceptually synonymous to multivariate regression (Borcard et al., 2011). The initial RDA analyses revealed that spatial dynamics explained 26.3% of overall variation, while temporal dynamics explained 5.6%, indicating strong spatial heterogeneity masking temporal changes. We therefore first examined spatial fish functional structure to determine how functional groups were distributed across the bay. Spatial functional structure was examined by applying K-means clustering to the entire CWM trait table with all sampling locations across all years (the R-package NbClust was used to determine the optimal number of clusters) and SIMPER analysis was used to characterize differences between the resulting clusters. K-means clustering is a least squares partitioning method for finding data groupings, while SIMPER assesses the contribution of individual variables to the dissimilarity between groups (Borcard et al., 2011; Clarke, 1993). Initial analyses revealed strong spatial differentiation between two clusters corresponding to inner- bay and outer-bay communities (Figure 1; Figure S1) and we therefore proceeded by

132 examining fish functional structure in each cluster individually, to examine temporal dynamics without the overwhelming effect of spatial heterogeneity.

Figure 1 | Map of the Bay of Somme showing all sampling sites throughout the entire time series, with colors corresponding to inner and outer-bay functional clusters. Survey zones and their corresponding depths are shown. For maps of environmental variables used in this study please see Figure S2.

To further characterize overall fish functional structure, we also built a multidimensional functional-trait space, where species are arranged according to their functional relatedness (Mouillot et al., 2013b). The functional space was created by applying principal coordinates analysis (PCoA) to a Gower dissimilarity matrix of the species by

133 functional trait table for the entire Bay of Somme, and was primarily examined using the first two principal coordinate axes, which cumulatively explained 54% of the total variance. PCoA is an ordination method for examining similarity among objects by transforming a dissimilarity matrix into a multidimensional space (Borcard et al., 2011). Building a functional space allows visualizing overall functional structure, determining whether changes in functional structure are driven by few or several species, and identifying similarities among impacted species. Functional space was built with PCoA as this ordination method is compatible with Gower’s distance, which can integrate multiple types of traits (e.g., continuous and categorical).

Temporal functional dynamics were examined by applying principal component analysis (PCA) to an annual CWM trait table for each cluster. PCA is an ordination method used to visualize the structure of a dataset and identify similarities or patterns among objects

(Borcard et al., 2011). In contrast to PCoA, PCA uses continuous data and ordinates communities via pairwise Euclidean distances (i.e., similarity). Significant changes in temporal functional structure were assessed by testing for changes in the first and second PCA axes using linear regressions. PCA loadings (functional traits most associated with PCA axes) were then examined to determine which functional traits best explained temporal variation, and the dynamics of each trait were tested using linear regressions to identify which traits significantly increased or decreased in relative abundance through time. We further examined temporal changes in functional structure by examining changes in species’ abundances in the functional space and by calculating annual abundance-weighted community centroids.

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4.3.5. Influences of environmental factors on spatial fish functional structure

We tested the influences of environmental factors in driving spatial variation in fish functional structure between clusters using a partial redundancy analysis (pRDA) designed to control for and remove the influence of temporal variation. pRDA is a constrained RDA that removes the effect of one or more explanatory variables prior to analysis (Borcard et al., 2011). Spatially- resolved data were unavailable prior to 1998, and thus spatial environmental variables represent the spatial regime for the time period 1998 – 2012. However, preliminary analyses showed that spatial differences between clusters were maintained throughout the entire time series (Figure S1), indicating consistent spatial variation in environmental drivers. Spatial variables included depth, SST, salinity, chlorophyll-a, suspended sediment, and bed shear stress. Significance of the pRDA was tested using Monte-Carlo permutation tests and the most parsimonious models were identified using forward selection according to Variance

Inflation Factors (VIF), F-statistics, and P-values (Borcard et al., 2011).

4.3.6. Influences of environmental factors on temporal fish functional structure

Spatially-resolved environmental data were unavailable prior to 1998; however, mean-annual environmental data for the entire Bay of Somme were available for the overall time series. We therefore examined temporal relationships between environmental variables and fish functional structure in each overall cluster. We tested relationships between environmental variables (AMO, NAO, SST, salinity, chlorophyll-a, and suspended sediment) and the first principal component (PCA axis 1) of temporal fish functional structure in each cluster using cross correlations of three-year moving averages (Figure 3b, c). Cross correlation identifies significant correlations in time series while accounting for potential time lags between variables, and thus can i) identify apparent cause-effect relationships because a causal variable

135 must come before an effect variable in temporal sequence, and ii) identify drivers with time- lagged ecological effects. Moving averages were used to reduce stochastic variation and highlight long-term tendencies. Cross correlations were first ran using standard inference testing (rcritical with d.f. = N – 2), and secondly re-run with modified rcritical values to account for temporal autocorrelation, using the correlogram product method of Clifford et al. (1989), where the standard deviation of the correlation between two processes is calculated as the square root of the weighted product of their autocorrelation functions (ACFs). This increases the correlation coefficient required to achieve significance (rcritical), as temporal autocorrelation can inflate Type-I error due to lowered degrees of freedom.

4.3.7. Influences of fisheries landings on temporal fish functional structure

As we were unable to examine the impact of fishing pressure in the EEC on fish functional structure in the Bay of Somme at the community level, we examined the relationship between fisheries landings and abundance for each species individually (34 with available data). Thus we were able to determine whether changes in the abundance of each species in the bay were likely driven by fishing in the EEC. We examined landings for each species individually rather than compiling landings for the entire EEC community, as few dominant species comprise the majority of overall landings. We tested relationships between changes in the abundance of individual species and their corresponding landings within each cluster using linear regression models. Abundance data were log10(x+1) transformed before analyses.

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4.4. Results

4.4.1. Spatial fish functional structure

We found two distinct spatial functional clusters corresponding to inner and outer-bay communities (Figure 1). Fish communities in the inner bay were mainly characterized by benthopelagic planktivores and piscivores, while fish communities in the outer bay were characterized by demersal benthivores (SIMPER analysis, top 50% of cumulative variation).

Functional space revealed that while inner and outer-bay communities were primarily characterized by differences in habitat preference and trophic guild, communities in both zones were dominated by fishes with low trophic level, low size and age at maturity, and small offspring (Figure 2a-c).

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Figure 2 | Fish functional space showing community trait structure along with temporal changes in species’ abundances. a) Functional trait composition for the overall Bay of Somme fish community. b, c) Species positions’ in functional space are plotted (points) in each cluster and sizes are scaled by the log of mean abundances throughout the overall time series. Convex hulls (colored polygons) represent the functional space containing all species in each cluster. d) Three-year moving averages and standard deviations of abundance- weighted community centroids along PCoA axis 2 in each cluster. e, f) Changes in the abundances of all species in each cluster; sizes are scaled by the log of the rate of change in species’ abundances, and colors indicate whether abundances decreased or increased.

4.4.2. Temporal changes in fish functional structure

Fish functional structure changed significantly over time in both inner and outer-bay communities, as we found significant changes in PCA axis 1 in both clusters (inner: F1,24 =

26.68, p < 0.0001; Figure 3c; outer: F1,24 = 23.02, p < 0.0001; Figure 3d). Examination of

PCA loadings revealed that temporal dynamics in inner-bay communities were mainly explained by relative decreases of planktivores and increases in fecundity and trophic level

(Figure 3a). Temporal variation in outer-bay communities was explained by relative increases

138 in age at maturity and trophic level, decreases in benthivores and increases in piscivores

(Figure 3b). Using regression analysis, we further found that relative length and age at maturity and fecundity significantly increased in both inner and outer-bay communities, which was accompanied by a relative increase in benthopiscivores and piscivores (Table 2).

In outer-bay communities there was also an increase in trophic level and temperature preference (Table 2). By contrast, planktivores decreased in inner-bay communities, while benthivores decreased in outer-bay communities (Table 2). Parental care also decreased in inner-bay communities (Table 2).

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Figure 3 | Principal component analysis (PCA) biplots showing temporal changes in fish functional structure in inner (a) and outer-bay (b) communities, along with the temporal dynamics (3-year moving averages and standard deviations) of AMO, SST, PCA axis 1, and overall fish abundance (dashed line) in each cluster (c, d). Note PCA 1 scores are shown in reverse order (positive to negative) to highlight temporal chronology in (a) and (b), and to compare trends with AMO and SST in (c) and (d).

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Table 2 | Changes in community-weighted mean (CWM) trait values in inner and outer-bay communities.

Inner Bay Outer Bay

Functional Trait Dynamic F1,24-value P-value Dynamic F1,24-value P-value

Age mature Increase 21.6 0.0001 Increase 14.67 0.0008

Length mature Increase 11.87 0.002 Increase 15.36 0.0006

Benthopiscivore Increase 11.71 0.002 Increase 5.08 0.03

Fecundity Increase 6.14 0.02 Increase 7.66 0.01

Piscivore Increase 5.33 0.02 Increase 20.22 0.0001

Trophic level Increase 13.92 0.001

Temp. Increase 5.79 0.02 preference

Parental care Decrease 20.37 0.0001

Benthivore Decrease 6.9 0.01

Planktivore Decrease 14.09 0.001

The first axis of functional space (PCoA axis 1) was primarily characterized by differences in water-column position, while the second axis (PCoA axis 2) corresponded to differences in trophic level, length and age at maturity, offspring size, and trophic guild

(Figure 2a-c). Consequently, there was little temporal change in community centroid movement along the first axis of functional space, which corresponded more to spatial differences between clusters. However there was a major change in the second axis (PCoA axis 2) as species that strongly declined in abundance were located in the upper half of the functional space, corresponding to low trophic level, early maturation, and small reproductive size (Figure 2d-f). Further examination of functional space revealed that overall temporal changes in fish functional structure were driven by decreasing abundances of the most dominant species, rather than increases in subordinate species (Figure 2e, f). Thus, temporal dynamics were characterized primarily by decreases in two functional groups – demersal

141 benthivores and benthopelagic planktivores – with r-selected life history traits, which lead to relative increases in length and age at maturity, fecundity and trophic level over time.

Furthermore, we found that there was a roughly 80% decrease in overall fish abundance in the bay throughout the time series, highlighting that the long-term dynamic of fish communities in the Bay of Somme was a considerable decrease in abundance.

4.4.3. Drivers of spatial and temporal dynamics

Spatial differences in fish functional structure between inner and outer-bay clusters were primarily driven by strong gradients in depth and bed shear stress, as the most parsimonious model included only these two variables (pRDA; F2,46 = 51.7, p < 0.001; Figure 4 Figure S2).

Inner-bay sites were characterized by shallow depth and high bed shear stress associated with estuary discharge, while outer-bay sites were deeper with lower bed stress. Salinity was also associated with differences between spatial zones, as the inner-bay had lower salinity due to freshwater input; however, given strong covariation with both depth and bed stress, salinity was not retained in the most parsimonious model.

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Figure 4 | Results of partial redundancy analysis showing significant environmental drivers of differences in fish functional structure between inner and outer-bay clusters.

Initial cross correlation analyses with standard inference tests revealed that temporal changes in fish functional structure were strongly associated with changes in AMO and SST.

AMO was correlated with PCA axis 1 in the inner-bay for all time lags from 0 to 7 years (max correlation: r = 0.66, p < 0.001; Figure 3c), and with PCA axis 1 in the outer-bay for lags 0 to

6 years (max correlation: r = 0.64, p < 0.001; Figure 3d). SST was correlated to inner-bay

PCA axis 1 for time lags 2 to 8 years (max correlation: r = 0.65, p < 0.001; Figure 3c), and outer-bay PCA axis 1 for time lags 0 to 6 years (max correlation: r = 0.78, p < 0.001; Figure

3d). NAO was also correlated to both inner and outer-bay PCA 1 axes; however, these

143 correlations were for negative time lags, indicating changes in fish functional structure precluded a decrease in NAO. None of the other variables (salinity, chlorophyll-a, or suspended sediment) were correlated with PCA axis 1 in either cluster. Re-running analyses with increased rcritical values to account for temporal autocorrelation showed that AMO remained correlated with PCA axis 1 in the inner-bay for time lags 3 and 4 years, while SST was correlated to inner-bay PCA axis 1 for time lags 3 to 7 years and outer-bay PCA axis 1 for time lags 1 to 3 years. Thus, despite high temporal autocorrelation in the data, typical of ecological and environmental time series, associations between temporal changes in fish functional structure and warming remained significant even with rcritical values inflated by 40-

73%.

In the inner bay, fish abundances were significantly related to fisheries landings for only one species, Phycis blennoides, however this relationship was positive, indicating that abundances and landings increased simultaneously (F1,22 = 12.1, p < 0.01). In the outer bay, abundances were significantly related to landings for only three species, Limanda limanda,

Pleuronectes platessa, and Dicentrarchus labrax, however these relationships were again all positive, indicating that that landings did not drive changes in abundances (Limanda limanda:

F1,22 = 11.74, p < 0.01; Pleuronectes platessa: F1,22 = 7.15, p < 0.05; Dicentrarchus labrax:

F1,22 = 4.4, p < 0.05). The relationship between fisheries landings and abundance was non- significant for all other taxa in both the inner and outer bay. To further account for potential time lags, we also examined cross correlations between landings and species’ abundances for lags 1 – 5 years, and of 115 possible correlations in the inner bay (23 species x 5 lags), only two were negative and significant (Trisopterus luscus, 4-year-lag; Merlangius merlangus, 4- year-lag), and of 155 possible in the outer bay (31 species x 5 lags) only 1 was negative and significant (Scophthalmus maximus, 5-year-lag). Although both Trisopterus luscus and

Merlangius merlangus decreased in the inner bay, they did not contribute substantially to

144 overall change (i.e., not within top 20%ile), however Scophthalmus maximus had one of the largest increases in the outer bay.

4.5. Discussion

We found that fish communities in the Bay of Somme underwent substantial changes in functional structure characterized by a switch from r-selected to K-selected dominance. This functional switch, between fast and slow life histories, was associated with increasing sea surface temperatures linked to a warming phase of the AMO. The AMO switched from a cool to warm phase in the late 1990s, and such natural climate oscillations superimposed on man- made climate change can lead to accelerations in ocean warming (Cai et al., 2014; Moore et al., 2017; Ting et al., 2009). Therefore it appears the AMO interacted with long-term global warming, driving increases in SST in the Bay of Somme, which likely caused the pronounced changes in fish functional structure found here.

We found strong spatial structuring in the Bay of Somme driven by a gradient of depth and bed shear stress, which shaped functionally different communities in the inner and outer bay (Henriques et al., 2017). The inner-bay, more influenced by the estuary conditions of the

Somme River, was historically dominated by benthopelagic planktivores likely adapted to turbulent waters that maintain planktonic suspension and limit competition by demersal fishes

(Amorim et al., 2017; Lindegren et al., 2012; Uiblein et al., 2003; Ware and Thomson, 2005).

Outer-bay communities, associated with deeper waters and lower bed stress, were historically dominated by demersal benthivores likely adapted to the more stable marine conditions and stronger benthic energy channel (Blanchard et al., 2011; Griffiths et al., 2017; Lindegren et al., 2012). While these results follow known ecological patterns, we cannot exclude that differences between the inner and outer bay, particularly among pelagic species, could be

145 influenced by different sampling protocols, as catchability changes with depth, sampling gear, and survey effort.

Despite these distinct differences between inner and outer-bay communities, temporal dynamics were relatively similar; the dominant functional groups, characterized by r-selected life history traits (e.g., low trophic level, low age and size at maturity, small offspring) declined dramatically in abundance. Population depletions in these groups caused a shift in dominance toward piscivores and benthopiscivores, leading to increases in trophic level, length at maturity, and age at maturity. As the major driver of changes in fish functional structure was likely rising sea surface temperatures, it appears that K-selected piscivores and benthopiscivores may be more resistant to warming in this, and possibly other ecosystems.

Past studies have shown that K-selected fishes are highly impacted by fishing pressure but are often less responsive to climatic changes, while the opposite has been observed for r-selected fishes (Cheung et al., 2005; Graham et al., 2011; Lynam et al., 2017; Simpson et al., 2011).

While the apparent climate-tolerance of large-bodied piscivores and benthopiscivores may explain their relative increase in the Bay of Somme, it is also likely that density-dependent changes in competition, predation, and reproduction contributed to the increase given severe drops in the previously dominant functional groups (Bolnick et al., 2010; Finke and Denno,

2005; Pace et al., 1999). However, while we documented a relative increase in K-selected fishes under warming, we cannot exclude that warming will eventually impact these fishes but with a substantial time lag given their long lifespans and slow population turnover. Therefore an eventual decrease in K-selected fishes is possible, particularly through reproduction or recruitment failure.

Previous investigation of the taxonomic structure of fish communities in the Bay of

Somme found that long-term changes were largely explained by a decrease in cold-water species (Auber et al., 2017a). Here, by considering not only temperature preference but also

146 life history and trophic ecology, we found that, beyond declines in cold-water species, life history traits best explained fish community responses to warming. This not only offers support for the functional-trait approach, but also suggests that fish respond to climate warming most prominently through differences in their life history strategies, which will be critical to understanding and modelling organismal responses to future climate change

(Fossheim et al., 2015; Givan et al., 2018; Jones and Cheung, 2018; Miller et al., 2018).

Marine fish nurseries such as the Bay of Somme are dynamic ecosystems prone to fluctuating environmental conditions (Cloern et al., 1983; Ippen, 1966; Murrell et al., 2017).

Estuaries in particular experience high variation in salinity and sedimentation due to freshwater input (Ippen, 1966). Such naturally-variable environments generally favor r- selected fishes, which can rapidly respond to environmental fluctuations and flourish under dynamic conditions, whereas K-selected fishes are better competitors in stable environments

(Gadgil and Solbrig, 1972; King and McFarlane, 2003; Pianka, 1970). However, r-selected fishes are potentially vulnerable to warming due to their high environmental responsiveness

(Devictor et al., 2012; Perry et al., 2005; Schweiger et al., 2008). Although r-selected fishes can quickly recover from minor disturbances, the accelerated temperature increase in the Bay of Somme likely rendered the environment unfavorable, and impacted fishes may have emigrated to more suitable environments (Fossheim et al., 2015; Montero‐Serra et al., 2014).

Strong correlation between SST and fish functional structure across several time lags further indicates a long-lasting impact and insufficient community inertia to rebound from such rapid environmental change. Yet, although r-selected fishes quickly track environmental changes and can be highly impacted by warming in the short term (Simpson et al., 2011), they should have greater capacity for evolutionary adaptation given their rapid generation times

(Rijnsdorp et al., 2009). Additionally, the impact of climate warming on r-selected fishes is likely context dependent, and such fishes may flourish under climate warming in other

147 ecosystems, especially by shifting poleward. Consequently, lower-latitude ecosystems dominated by r-strategists will likely suffer declines under climate warming, as such environments may become unfavorable and fishes could shift poleward, whereas poleward ecosystems and ecosystems dominated by K-strategists may experience increases in r-selected fishes (Nicolas et al., 2011).

While we documented a major decline in r-selected fishes in parallel to rapid warming, changes in trophic structure and ecosystem functioning may have exacerbated the shift in functional structure (Chevillot et al., 2018; Chevillot et al., 2016) Given the relative increase in K-selected fishes in parallel to the decline in r-selected fishes, it is possible that trophic re- balancing reinforced community changes as K-selected piscivores may have over-exploited the already-declining r-strategists, leading to a negative feedback. Additionally, K-selected fishes that increased in abundance through time may have opportunistically shifted their diets toward alternative resources as r-selected fishes declined, further reorganizing ecosystem structure and functioning (Rooney et al., 2006).

The Eastern English Channel (EEC) also underwent a major community shift in the late 1990s, characterized by a decline in r-selected pelagic fishes in response to AMO- associated warming (Auber et al., 2015). The timing of the shift in the EEC corresponded to the shift documented here, and we additionally found that changes in functional structure between the two ecosystems were correlated (McLean unpublished data). Thus, it is likely that both the EEC and Bay of Somme were concurrently impacted by warming linked to the

AMO, which lead to declines in fishes with environmentally-sensitive life history traits.

However, the shift in the EEC occurred in the late 1990s, just prior to major declines in the bay, suggesting that biological responses in the EEC may have cascaded into the bay through recruitment failure (Ljunggren et al., 2010; Pankhurst and Munday, 2011; Payne et al., 2009).

Abrupt decreases in adult spawning fishes in the EEC could have a major impact on the bay,

148 potentially driving the observed declines, as larval input would have diminished. In the 20 years following the shift in the EEC, the fishes that decreased in abundance have not recovered, and the ecosystem remains in an alternative state (Auber et al., 2015). Thus, it is also possible that population declines in the Bay of Somme acted as a negative feedback through reduced replenishment, limiting recovery potential (Pankhurst and Munday, 2011;

Reis-Santos et al., 2013).

While there is no commercial fishery in the Bay of Somme, historical and contemporary fishing pressure in the EEC likely impacted fish communities in the bay through changes in spawning stock biomass, larval production, and recruitment dynamics

(Able, 2005; Gillanders, 2002; Lipcius et al., 2008; Vasconcelos et al., 2011). We did not identify EEC landings as a primary driver of fish abundances in the bay; however, EEC landings over the last thirty years have been dominated by species such as herring, sprat, plaice, and sole, which all decreased in the bay over time. Although most stocks have had steady or declining landings in recent decades, continued pressure is certainly impacting fish communities in the EEC and its nurseries. Long and steady exploitation can also reduce fish abundances without showing any statistical association, especially if fishing is unsustainable.

Furthermore, the English Channel and Northeast Atlantic are commercially important fishing grounds for multiple countries, and have a long history of over-exploitation (Myers et al.,

1996; Pauly et al., 2002; Pauly and Maclean, 2003). McHugh et al. (2011) and Molfese et al.

(2014) showed a progressive shifting baseline in the English Channel due to a century of overfishing and fishing down the food web. This historical pressure progressively eroded the fish community from dominance by large, slow-growing species to small, quickly reproducing species and commercially-untargeted sharks and rays (Molfese et al., 2014).

Historical overfishing likely rendered both the EEC and its nurseries more vulnerable to climatic changes due to increased dominance by r-selected species, which are more tolerant to

149 fishing but highly responsive to environmental changes (Auber et al., 2015; Kuo et al., 2016).

Additionally, contemporary declines in fishing pressure likely facilitated the increase in K- selected species, such as Scophthalmus maximus, which was negatively correlated with landings in the outer bay. Altogether, past overfishing and continued exploitation likely impacted fish communities in the bay, through both direct removal of spawning adults, as well as indirect cascades in predation and competition, and synergistic effects between fishing and climate are possible (Daan et al., 2005; McHugh et al., 2011; Molfese et al., 2014).

However, given the strong correlation between warming and the decline in multiple r-selected species, it appears that climate and not fishing was the main driver of changes at the community-scale. In support, our results highlight long-term decreases in fishes know to rapidly track environmental changes in parallel to increased dominance by species more vulnerable to fishing pressure.

Our results suggest that climate warming can profoundly alter functional structure in marine fish nurseries, which could have major implications for the stability of large marine ecosystems (LMEs) worldwide, as nurseries contribute to LME population maintenance

(Baptista et al., 2015; Beck et al., 2001; Liquete et al., 2016; Seitz et al., 2014). In the Bay of

Somme, declines in commercially-important species like plaice, sole, sprat and herring likely had negative, reinforcing impacts on fish stocks in the EEC (Auber et al., 2017a, 2015). We found that fishes bearing r-selected functional traits were highly impacted by warming, leading to a shift in dominance toward alternative functional groups with higher length and age at maturity and higher trophic level (Devictor et al., 2012; Jiguet et al., 2007; Pecuchet et al., 2017; Perry et al., 2005). Our results therefore indicate that ongoing climate change may impact marine fish nurseries through declines in overall abundance and shifts in dominance toward fishes with environmentally-tolerant traits. While our study was limited in spatial extent, because estuaries and nursery habitats generally favor r-selected strategists, our

150 findings are likely applicable to nurseries in other regions (Steele et al., 2009; Teichert et al.,

2017; Thomson and Lehner, 1976). For example, Teichert et al. (2017) showed that estuaries across the Northeast Atlantic were dominated by opportunistic species with life history traits adapted to unstable environments, while Thomson and Lehner (1976) showed that even rocky, intertidal pools (used as nurseries by coastal species (Dias et al., 2016)) favor r- selected strategists. Teichert et al. (2017) additionally showed that life history strategies were useful predictors of disturbance impacts on estuarine fish communities. Thus, dominance by opportunistic, r-selected fishes may indicate nurseries that could be heavily impacted by future warming.

Reorganizations in fish functional structure likely have major implications for ecosystem functioning as r and K-strategists differ markedly in nutrient cycling, energy transfer, and biomass turnover (Blanchard et al., 2011; Houk et al., 2017; Rooney et al.,

2006). Such changes will also have major implications for fisheries, as K-selected species are generally more vulnerable to fishing pressure (Cheung et al., 2005; Graham et al., 2011). For example, in the Bay of Somme, European seabass (Dicentrarchus labrax) and turbot

(Scophthalmus maximus) had large increases in relative abundance, but both species are considered fishing-vulnerable (Cheung et al., 2005; Froese and Pauly, 2012). Thus resource management should consider both the climatic and fishing vulnerability of communities to anticipate how future changes in environmental conditions and fishing pressure may impact marine ecosystems. In ecosystems dominated by species with r-selected functional traits, reduced fishing may alleviate the impacts of future warming. In contrast, as K-selected strategies appear more tolerant of warming, additional harvesting may be permissible for well-managed stocks with increasing abundances. With the emergence of functional-trait ecology, resource managers can also focus directly on fish functional structure, aiming to

151 enhance response diversity through a balance of life history strategies with different environmental sensitivities (Izzo et al., 2016).

While we documented a marked shift in fish functional structure, a common weakness of functional-trait studies is the inability to capture intra-specific and ontogenetic trait variation (Petchey and Gaston, 2006; Violle et al., 2007). Accordingly, we were unable to account for the influence of the relative composition of adults and juveniles on the spatial and temporal dynamics of functional structure. Future studies examining functional-trait dynamics in nurseries should incorporate demographic metrics such as size spectra and age distribution.

Examining community changes through biomass rather than abundance could also provide insight on how juvenile demographics influence functional structure, and help link changes in functional structure to ecosystem processes (de Bello et al., 2010; Leps et al., 2006).

Emerging approaches integrating intra-specific and ontogenetic trait variability will help clarify how changes in size and age classes influence fish functional structure in nurseries.

Marine fish nurseries are critical components of ecosystem landscapes, providing suitable habitats for the development of commercially important fish species (Dahlgren et al.,

2006; Gillanders et al., 2003; Tournois et al., 2017; Vasconcelos et al., 2010). Nurseries are therefore extremely important to maintaining fish communities and ecosystems, making them priorities for resource management. Despite the importance of nurseries, few studies have examined the impacts of climate warming on functional structure in nursery communities.

Our results indicate that communities dominated by fishes with r-selected traits are highly responsive to environmental change and may be more vulnerable to warming than previously recognized. Our results provide a critical warning, indicating that future climate change will likely affect marine fish nurseries worldwide, and immediate conservation efforts are needed to reduce the potential impacts. If large-scale studies confirm that marine fish nurseries are

152 being heavily impacted by climate change globally, protection of fish nurseries must be immediately prioritized in ecosystem-based management.

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4.6. Supplementary material

Supplemental Figure 1 | Results of a preliminary principal components analysis (PCA) examining temporal variation in both the inner and outer bay together. The majority of overall variation in fish functional structure (PC1) was explained by spatial differences between clusters, which masked temporal variation within each cluster (PC2). Therefore, temporal dynamics of fish functional structure were examined in each cluster individually.

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Supplemental Figure 2 | Maps of spatial environmental variables used in this study. Here, variables were averaged across the entire time series in order to visualize overall spatial gradients.

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5. A climate-driven functional inversion of connected marine

ecosystems5

5Published as McLean M., Mouillot D., Lindegren M., Engelhard G., Villéger S., Marhcal P.,

Birnd’Amour A., Auber A. 2018. A climate-driven functional inversion of connected marine ecosystems. Current Biology 28:1-7

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5.1. Preface

In the EEC and Bay of Somme, Chapters 2 – 4 documented important decreases in the abundances of r-selected species known to rapidly track environmental changes and pelagic species with high mobility. The results of these studies and prior studies by Auber et al.

(2017a, 2015) indicated a potential northward shift in species distributions and abundances in response to rapidly changing environments. For example, as the EEC warmed, sensitive species may have shifted northward in search of more favorable environmental conditions, either through active migration, passive advection of eggs and larvae, or through latitudinal differences in mortality, recruitment, and survival. If so, we should observe opposite dynamics in the southern North Sea where the fishes that decreased in the EEC and Bay of

Somme would have concurrently increased. Additionally, prior to this thesis, no study had previously examined changes in fish functional structure in the southern North Sea, and it remained unknown how fish functional structure had changed in recent decades, despite major environmental changes such as rapid warming. Therefore, in Chapter 5, I examined and compared temporal trends in fish functional structure between the EEC and southern North

Sea fish communities at the ecosystem scale (i.e., gamma diversity scale), and related the observed changes to environmental variation and long-term fisheries dynamics.

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5.2. Introduction

Climate warming can impact species and communities through geographic range shifts, phenological mismatches, and local extinctions (Poloczanska et al., 2013; Wiens, 2016). In marine systems the most profound changes originate from shifts in species’ distributions and abundances due to increasing sea surface temperatures (SST), which have risen by 0.11°C per decade in the last forty years (IPCC, 2014; Perry et al., 2005) Furthermore, naturally- occurring, ocean-wide climate oscillations such as North Atlantic Oscillation (NAO) and

Atlantic Multidecadal Oscillation (AMO) can impact marine ecosystems through rapid changes in SST and oceanographic processes (Alheit et al., 2014; Ting et al., 2009). Past studies have documented major changes in species’ abundances and fishery catches following exceptional warming events caused by such climate oscillations (Alheit et al., 2014;

Lindegren et al., 2013). For instance, pronounced fluctuations of sardine and anchovies have been explained by multi-decadal oscillations and abrupt transitions between cold and warm regimes in the North Pacific (Lindegren et al., 2013). While previous studies have linked sea surface warming and natural climate oscillations to changes in fisheries catches or species distributions (Cheung et al., 2013), it is not yet clear whether these processes can interact to impact ecosystems through shifts in their functional structure (i.e., the composition of species’ functional traits). The extent and speed at which functional groups can shift in dominance under sea surface warming and climate oscillations is largely unknown, while it could determine the impact of global change on food-web dynamics and ecosystem functioning.

Here, we used over 30 years of fish monitoring data and an extensive compilation of ecological traits to examine the dynamics of fish functional structure in the Eastern English

Channel (EEC) and Southern North Sea (SNS) (Figure 1a) under a warming phase of the

Atlantic Multidecadal Oscillation (AMO), a 60 – 80-year climate cycle affecting sea surface temperatures (SST) in the North Atlantic (Ting et al., 2009). We characterized functional

159 structure using ten traits (Table S1) related to life history, habitat use, and trophic ecology for

73 fish taxa in the EEC and 110 in the SNS. To examine the temporal dynamics of fish functional structure in the two ecosystems, we used a multidimensional functional-trait space where species’ positions reflect their functional relatedness (Mouillot et al., 2013b). We then used Granger causality tests (see methods) to assess the influences of SST, AMO, chlorophyll-a, and fisheries landings on changes in fish functional structure through time, with cross-correlation analyses used to identify potential time lags between variables.

5.3. Methods and materials

5.3.1. Fish survey methods

Fish abundance data for the EEC came from the Channel Ground Fish Survey (CGFS)

(Coppin and Travers-Trolet, 1989), an annual monitoring campaign that uses stratified random sampling to survey fish communities at 90 to 120 stations across ICES (International

Council for Exploitation of the Sea) area VIId each October. During each survey, a 3-m vertical opening bottom trawl (i.e., GOV trawl) with a 10-mm mesh codend is towed for 30 min at an average speed of 3.5 knots. In each survey, fishes are identified and counted, and resulting abundances are standardized to numbers of individuals per km2. The data included the period from 1988 to 2011. Fish monitoring data for the SNS came from the International

Bottom Trawl Survey (IBTS) (Verin, 1992), a similar annual campaign conducted in February in approximately 150, 1° longitude by 0.5° latitude survey rectangles covering the entire

North Sea. The IBTS also uses a GOV trawl, which is towed for 30 min at an average speed of 4 knots. IBTS abundance estimates are also standardized to numbers of individuals per km2. These data were available from 1983 to 2015 for ICES areas IVa, IVb and IVc. A

160 potential concern with these surveys is that the survey gear is not well-adapted for sampling pelagic fishes, and changes in the depth distribution of pelagic fishes could influence survey findings. However, sampling methods have remained consistent through time, thus potential sampling biases have not changed and community changes documented in the surveys should reflect changes in community structure. Furthermore, the IBTS survey campaign has been previously shown effective for examining the temporal dynamics of both demersal and pelagic fishes (Hiddink and Ter Hofstede, 2008), and the IBTS surveys are reliably used for pelagic stock assessment. A second potential concern was the possibility that differences in community structure in the two ecosystems could result from different sampling seasons.

However, our study focused on global trends in each ecosystem throughout the overall time series. Therefore, temporal changes in community structure highlighted inter-annual and not seasonal variation within each ecosystem individually.

As both fish communities and environmental conditions in the North Sea are highly stratified with depth (Kenny et al., 2009) and because the northern North Sea is open to the

North Atlantic Ocean, the SNS was selected via k-means clustering of spatial fish functional structure for the overall time series. The resulting clustering for the SNS strongly corresponded both to the 50-m depth contour, which has previously been documented as a natural boundary for the SNS (Kenny et al., 2009), and to taxonomic clustering. Furthermore, the resulting clustering corresponded to a previous investigation of spatial fish functional structure across the North Sea (Pecuchet et al., 2017), highlighting the consistent division of taxonomic and functional structure near the 50-m depth contour. Furthermore, changes in fish functional structure were examined throughout the entire North Sea, and similar trends were repeatedly found, indicating that functional changes in the North Sea were spatially consistent and robust to the choice of geography. Finally, because the IBTS survey occurs in February, at the beginning of each year, fish monitoring data from the SNS were matched to the

161 environmental data from the previous year, so that fish data were not matched with environmental data that had not yet occurred (in the ten months following the fish surveys).

5.3.2. Environmental and fishing data

The AMO refers to a 60–80 year natural climate cycle affecting sea surface temperature across the entire North Atlantic Ocean (Edwards et al., 2013). Unsmoothed data for the AMO came from the US National Oceanic and Atmospheric Administration (National Oceanic and

Atmospheric Administration (NOAA, US), 2014). Mean annual sea surface temperature data were derived from the Hadley Centre for Climate Prediction and Research’s freely available

HadISST1 database (Rayner et al., 2003). Mean annual chlorophyll-a data came from the Sir

Alister Hardy Foundation for Ocean Science’s Continuous Phytoplankton Recorder database

(SAHFOS).

Fisheries landings data for the period 1983 – 2010 came from the ICES Catch

Statistics Database (ICES). Fisheries landings (tons per year) were first extracted for all available species for ICES division VIId for the EEC and divisions IVc and IVb for the SNS.

To calculate pelagic landings, demersal landings, and total landings, data were combined for all available i) pelagic species (EEC: n = 9; SNS: n = 13), ii) demersal species (EEC: n = 47;

SNS: n = 44), and iii) and for all overall species (EEC: n = 55; SNS: n = 58) that were observed during the CGFS and IBTS surveys. Thus, we derived pelagic, demersal, and total fisheries landings data that best reflected the actual fish communities of the EEC and SNS assessed during fisheries monitoring campaigns. Landings data were thus favored over fishing mortality indices, which are often based on a few representative species, while landings data were available for over 50 species per ecosystem, and are therefore better adapted for

162 community-level analyses. Prior to all statistical analyses, fisheries landings data were log10 transformed.

5.3.3. Functional traits and functional space

We selected ten functional traits known to influence species responses to environmental changes and impacts on ecosystem processes, particularly in these ecosystems (Engelhard et al., 2011; Somerfield et al., 2008) (Table S1). Functional traits incorporated life history, trophic ecology, and habitat associations. Functional trait data came primarily from FishBase

(Froese and Pauly, 2012) but also from primary literature when data were unavailable or inconsistent on FishBase. We compiled functional trait data for 129 taxa (116 species and 13 unspecified genera). The multidimensional functional space was then created by applying principal coordinates analysis (PCoA) to a Gower similarity matrix of the taxa by functional trait table, and was primarily examined by plotting the first two principal coordinate axes

(Mouillot et al., 2013b), which cumulatively explained 45% of the total variance. Functional space was additionally examined using the third and fourth principal coordinates axes which cumulatively explained 22% of the total variance, thus the first four axes of functional space accounted for 67% of the total variance and additional axes were not examined. Community centroids within functional space were then computed for each year by the abundance- weighted positions of all taxa on the first two principal coordinate axes (Laliberté and

Legendre, 2010), and centroid movements along the first axis were used to visually examine changes in functional trait structure through time (Figure 2e, f).

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5.3.4. Data analyses

All data analyses were conducted using R-statistics (Ver. 3.3.2; R Core Team) (Ihaka and

Gentleman, 1996). Temporal Changes in environmental variables and fisheries landings were first assessed by simple linear regressions of each variable against time. For each ecosystem, temporal dynamics of fish functional structure were examined by first generating tables of community-weighted mean trait values using the standardized (i.e., scaled) functional traits and log10(x+1) of taxa abundances for each year, which were then ordinated using principal component analysis (Figure S1). The first PCA axes of the resulting ordinations were then used as the metrics of fish functional structure for Granger causality analyses. Multivariate regression trees (De’Ath, 2002) were used to determine whether there were marked shifts in fish functional structure in each ecosystem over time, and to identify the years when they occurred. By considering time as a constrained variable, multivariate regression trees perform chronological clustering suitable for detecting shifts in multivariate time series. Multivariate regression trees are assessed by finding the optimal partitions that minimize relative error and explain the greatest amount of variation (Borcard et al., 2011). Significant differences in fish functional structure between the resulting time periods (before and after the functional shifts) were then tested by applying analysis of similarity with 999 permutations to the community- weighted mean functional trait matrices with time period as the tested factor.

To examine the contribution of life history traits to changes in fish functional structure beyond the shift in pelagic and demersal dominance, we built linear models testing the effect of the interaction of ecosystem and life history traits on changes in species’ abundances before and after the shift in each ecosystem. These models revealed whether the relationships between changes in species’ abundances and trait values differed between the ecosystems, e.g., lower age at maturity lead to greater decreases in abundance in the EEC but greater increases in abundance in the SNS. For each model, trait values (not including trophic level

164 and parental care) and changes in species’ abundances were log transformed for normality and linearity.

We began by testing the influence of all environmental and fishing variables on fish functional structure (PCA axis 1 of temporal functional dynamics; Figure S1c, d) using multiple linear regression to identify the most influential variables. Initial multiple regression models identified AMO as the only significant driver of fish functional structure in the in the

EEC, while both demersal landings and SST were identified as significant drivers in the SNS.

However, an issue with analyzing time series data with standard statistical models such as multiple linear regressions or generalized linear models is that such models ignore i) temporal autocorrelation and ii) potential time lags between correlated processes and thus do not consider whether changes in one variable occur before or after another in temporal sequence.

A cause cannot occur after its effect in temporal sequence, and thus standard regression models only reflect correlation without considering predictive relationships where changes in a causal driver precede and thus predict changes in an effect. For example the shift in fish functional structure in the EEC occurred in 1997, while pelagic landings sharply increased in

2000, suggesting a secondary effect, as the shift in community structure occurred prior to changes in fishing.

Granger causality tests (Granger, 1969) were therefore performed in order to differentiate between simple correlations and apparent causal relationships between environmental and fishing variables and fish functional structure. A recently developed method for examining causality in non-linear time series, convergent-cross-mapping (CCM)

(Sugihara et al., 2012), was first attempted in lieu of Granger Causality; however, reliable results could not be obtained as CCM can be unreliable for time series of only thirty time steps, and methods for applying CCM to shorter time series are only beginning to emerge (Ma et al., 2014). Granger causality analysis is a linear modelling method that identifies apparent

165 causality by satisfying two criteria: i) a causal factor should precede an effect, and ii) incorporating historical values of a causal factor leads to significantly improved prediction of an effect (Granger, 1969). If a variable is identified as significant by Granger causality, it is said to “Granger cause” the effect variable, as true causality can never be proven in the absence of controlled experiments (Granger, 1969). Prior to Granger causality analyses, all variables were made stationary by taking first differences to account for temporal autocorrelation. In the SNS, analyses were applied for the time series ranging from 1983 to

2013, as data were not available for all environmental drivers in 2014 and 2015. Granger causality was executed in the R package vars, which uses Akaike’s Information Criterion

(AIC) to identify the optimal number of historical predictor values for generating models.

Finally, cross correlation analyses were applied following Granger causality to determine the time-lags of effect between significant environmental drivers and fish functional structure in both ecosystems. The results of cross correlations were interpreted by identifying the optimal significant time-lags of environmental variables that had the highest correlation with fish functional structure.

5.4. Results

Warming was clearly reported in both ecosystems as annual SST increased by 0.28°C (±0.09) per decade in the EEC (1983 – 2015; F1,31 = 10.36; p < 0.01; Figure 1d) and 0.41°C (±0.1) per decade in the SNS (1983 – 2015; F1,31 = 16.60; p < 0.001; Figure 1b). Likewise, the AMO increased significantly over time (F1,31 = 33.88; p < 0.0001) as it entered a warming phase during the 1990’s (Ting et al., 2009) (Figure 1b, d). While there was no significant trend in chlorophyll-a in the EEC, it significantly increased in the SNS by roughly 30% over the entire period (F1,31 = 5.22; p < 0.05; Figure 1b).

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Figure 1 | Map of the two study ecosystems along with their long-term environmental and fishing variability. Locations of the Eastern English Channel (light blue area) and Southern North Sea (dark blue area) ecosystems (A) and their long-term dynamics in Atlantic Multidecadal Oscillation (AMO) (purple time series), sea surface temperature (red time series), and chlorophyll-a (green time series) (B, C), as well as demersal (dark yellow time series), pelagic (orange time series), and total (brown time series) fisheries landings.

Demersal fisheries landings remained relatively stable in the EEC with no significant long-term change. By contrast, pelagic landings increased through time, notably between

2000 and 2005 (F1,21 = 9.98; p < 0.01; Figure 1e) while total landings also increased, largely tracking pelagic landings (F1,21 = 5.2; p < 0.05; Figure 1e). In the SNS, demersal landings progressively decreased (F1,26 = 39.16; p < 0.0001), while pelagic landings increased (F1,26 =

7.24; p < 0.05; Figure 1c), however the increase in pelagic landings occurred primarily before

1990. While total landings in the SNS have progressively decreased since the mid-1990s, there was an initial spike in the early-1990s, leading to no significant long-term change.

We first examined temporal changes in fish functional structure using annual community-weighted mean trait values on principal component axes (see methods; Figure

S1). Based on resulting dynamics, we then applied multivariate regression trees with analysis of similarity to identify whether there were significant shifts in fish functional structure and in which years they occurred. We identified a significant shift in 1997 in the EEC (R2 = 0.60; p

< 0.01) and in 1998 in the SNS (R2 = 0.39; p < 0.01). These years were then used to examine

167 changes in species abundances in functional space and compare trends between ecosystems.

Functional space (Figure S2) revealed that these shifts were primarily driven by rapid changes in two species groups, one pelagic and one demersal (species clusters on left and right sides of functional space), which simultaneously decreased in the EEC and increased in the SNS

(Figure 2a, b, c), indicating a northward shift between ecosystems. Interestingly, despite major differences in habitat use and diet, both fish groups were characterized by r-selected life history traits such as low age and size at maturity, small offspring, and low trophic level

(Figure 2a, b, c). However, changes in pelagic fish abundances were much more pronounced, causing fish community centroids to move abruptly across the functional space, highlighting the inversion in functional structure between the two connected ecosystems (Figure 2d, e, f;

Figure S3): the EEC became relatively less dominated by r-selected pelagic fishes and more dominated by K-selected (e.g., high age and size and at maturity, large offspring, high trophic level) demersal fishes, while the SNS became relatively less dominated by K-selected demersal fishes and more dominated by r-selected pelagic fishes.

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Figure 2 | Inversion in fish functional structure between the Eastern English Channel (EEC) and Southern North Sea (SNS). Multidimensional fish functional space showing functional-trait structure (A) along with changes in species’ mean abundances in the EEC (B) and SNS (C) before and after the functional shift (EEC: 1997; SNS: 1998). (B, C) Red and green points indicate species that decreased or increased in abundance between the two periods, respectively, while point sizes are scaled by the log of absolute change in abundance, and species with changes less than 1 individual/km2 are not shown. Polygons represent the functional space (i.e., convex hulls) containing all species in each ecosystem (EEC = light blue; SNS = dark blue; combined = black). (D) Abundance-weighted fish community centroids for the periods before and after the functional shift in both ecosystems. (E, F) Temporal dynamics of fish community centroids along the first axis of functional space with vertical lines indicating the functional shift. See also Figure S2, which shows the overall functional space for both ecosystems, Table S1, which lists the functional traits, Figure S3, which shows changes in the distribution and abundance of pelagic fishes in the two ecosystems, and Table S2, which shows changes in species’ abundances before and after the functional shift.

To further examine the contribution of life history traits to the shift in fish functional structure, we tested whether relationships between changes in species’ abundances and traits differed between ecosystems. We thus used linear models to test the effect of the interaction between ecosystem and each life history trait (and trophic level) on changes in species’ abundances before and after the functional shift. We found significant interaction terms for

169 age at maturity (interaction coefficient = -2.18; F3,171 = 10.09, p < 0.01), length at maturity

(interaction coefficient = -1.92; F3,179 = 9.1, p < 0.01), offspring size (interaction coefficient =

-0.58; F3,175 = 8.16, p < 0.01), parental care (interaction coefficient = -0.61; F3,179 = 6.78, p =

0.05), and trophic level (interaction coefficient = -6.2; F3,179 = 6.7, p = 0.05), indicating opposite trends between ecosystems: fishes with lower age and size at maturity, smaller offspring, lower parental care, and lower trophic level (r-selected traits) simultaneously decreased in the EEC and increased in the SNS regardless of whether they were pelagic or demersal (Figure 3).

Figure 3 | Inverse relationships between changes in fish abundances and life history traits in the Eastern English Channel (EEC, light blue) and Southern North Sea (SNS, dark blue). Fishes with the largest decreases in the EEC and largest increases in the SNS had low age (A) and size at maturity (B), small offspring (C), low parental care (D), and low trophic level (E) (r-selected traits).

A series of Granger causality tests then identified AMO as the only significant causal driver of changes in fish functional structure in the EEC (F5,14 = 4.84; p < 0.01; Figure 4a), while both AMO and SST were identified as significant causal drivers in the SNS (AMO F1,50

= 13.33; p < 0.001; SST F2,44 = 6.86; p < 0.01; Figure 4b). Neither chlorophyll-a nor fisheries landings had significant causal influence on fish functional structure in either ecosystem.

Cross-correlation analysis then revealed a 1-year optimal time-lag (i.e., highest correlation) between changes in AMO and fish functional structure in both ecosystems (EEC r = 0.78; p <

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0.001; SNS r = 0.75; p < 0.0001), and a 2-year optimal time lag between changes in SST and fish functional structure in the SNS (r = 0.63; p < 0.001).

Figure | Ranked drivers of changes in fish functional structure. Causal influences of the Atlantic Multidecadal Oscillation (AMO), sea surface temperature (SST), chlorophyll-a (Chl- a), pelagic landings (Lpel), demersal landings (Ldem), and total landings (Ltot) on fish functional structure in the Eastern English Channel (A) and Southern North Sea (B) as revealed by Granger causality tests. Significant causal drivers are plotted alongside PCA axis 1 in both ecosystems. ** = p < 0.001, * = p < 0.01. Optimal time lags are indicated for significant drivers. See also Figure S1, which shows the full PCA analysis.

5.5. Discussion

Previous studies have documented pronounced changes in taxonomic community structure following rapid warming events where species shift poleward into adjacent ecosystems

(Vergés et al., 2014; Wernberg et al., 2013). Our results further indicate that climate-driven

171 changes in species’ abundances and distributions can lead to functional reorganization of entire species assemblages, particularly between connected ecosystems. However, not all impacted species concurrently decreased in the EEC and increased in the SNS, suggesting that the inversion in functional structure was driven not only by distribution shifts between ecosystems, but also by opposite trends in mortality, recruitment, and survivorship of functionally-similar species. While we observed a pronounced shift in a single taxonomic group (fishes), shifts in fish communities have been documented alongside shifts in planktonic and benthic communities (Lindegren et al.,2018; Vergés et al., 2014; Wernberg et al., 2013), and recent experiments show synchronous responses to warming across diverse phylogenetic and taxonomic groups (Smale et al., 2017). As fishes are the dominant vertebrates in marine food webs and strongly influence ecosystem processes and stability

(Bascompte et al., 2005; Duffy et al., 2016), this shift in fish functional structure was likely mirrored throughout the ecosystems, resonating across multiple taxonomic and trophic groups

(Beaugrand, 2004).

This functional shift was primarily characterized by inverse changes in the abundance of pelagic fishes, because although both demersal and pelagic fishes decreased in the EEC and increased in the SNS, the change in pelagic fishes was more pronounced. Previous studies have shown ecosystem changes driven by shifts between pelagic and demersal commercial fishery species in relation to climate change (Lindegren et al., 2012). However, here, we characterized climate-driven changes in the functional structure of entire fish assemblages in two adjacent ecosystems. More interesting than the shift in pelagic and demersal dominance was the finding that the most impacted fishes were characterized by r-selected life history traits related to reproduction, population turnover, and generation time. By examining communities through the lens of functional traits, we found that, beyond habitat type or diet, species with r-selected life-history traits were most responsive to temperature rise, reinforcing

172 that life-history cycles determine fish responses to climate warming. Previous studies have shown that r-selected strategists are highly responsive to warming because short generation times favor faster population responses (Devictor et al., 2012; Pearson et al., 2014; Pecuchet et al., 2017; Perry et al., 2005). However, no study has demonstrated the impacts of r-selected species responses on the functional structure of connected ecosystems over large temporal and spatial scales. Our results highlight that not only are r-selected species more responsive to climate change, but given their quick generation times, rapid sexual maturity, and high dispersal rates, their responses can abruptly shift the functional structure of marine ecosystems.

Large-scale climate oscillations can impact communities through changes in multiple ecological processes (Hallett et al., 2004), and the speed of this shift was probably enhanced by oceanographic processes linked to the AMO, including large inflows of Atlantic water through the English Channel (Edwards et al., 1999). Thus, the rapid shift in distribution and spatial reallocation of r-selected pelagic fishes in the SNS may have resulted from both active migration in pursuit of warm water-masses, as well as passive advection of eggs and larvae.

Variability in larval survival and recruitment success could have also contributed to the rapid shift in abundance and distribution. Although chlorophyll-a was not identified as a driver of changes in functional structure, increased productivity in the SNS may have exacerbated the shift, as r-selected species can rapidly increase their populations in response to available resources. Additionally, associated changes in planktonic composition could have reinforced the transition (Beaugrand, 2004). In particular, long-term changes in dominance from large to small zooplankton may have benefitted pelagic fishes, which are less dependent on larger zooplankton than the planktonic life stages of demersal fishes (Beaugrand, 2004).

Although we found that changes in fish functional structure were primarily driven by climate, fishing is a well-known driver of population dynamics and the relative abundances of

173 target vs. non-target species in these ecosystems. North Atlantic ecosystems have been intensively fished since industrialization in the 20th century (Pauly et al., 2002), and progressive overfishing, particularly in the EEC, has led to a long-term shifting baseline as historically abundant species such as spurdog, cod, and ling have been replaced by small pelagics and commercially-untargeted elasmobranchs (Molfese et al., 2014). Thus, moderate to low exploitation of many small, fast-growing species compared to the historically high fishing pressure on many commercially-important demersal species likely rendered these ecosystems more susceptible to climate warming, as they became dominated by species with environmentally-sensitive life-history traits (Daan et al., 2005; McHugh et al., 2011; Molfese et al., 2014). While fishing was not identified as a main driver of the functional shift, pelagic landings have increased in the EEC while overall landings have decreased in the SNS (ICES).

Hence, the decrease in pelagic fishes in the EEC in parallel to an increase in the SNS was potentially exacerbated by fishing, as pelagic harvesting in the EEC may have prevented recovery while reduced harvesting in the SNS may have facilitated the increase. However, as inverse changes in functional structure between ecosystems were primarily driven by a northward shift of r-selected pelagic fishes in only 1-2 years, it is unlikely that fishing was a main driver, as fisheries impacts tend to manifest progressively over longer periods (Pauly et al., 2002). Yet, we cannot dismiss that both historical overfishing and contemporary changes in fishing pressure likely contributed to the increase and spatial reallocation of pelagic fishes in the SNS, either directly or through indirect changes in trophic interactions and predation pressure (Daan et al., 2005).

Given widespread phenological mismatches among marine organisms under climate change (Edwards and Richardson, 2004), it is also possible that long-term changes in seasonal movements of pelagic and demersal fishes could have influenced our findings. Fisheries monitoring campaigns are routinely conducted during the same month each year. Therefore,

174 year-to-year changes in fish community structure could be impacted by phenological mismatches between community patterns and fisheries campaigns. Phenological shifts could arise from behavioral adaptations such as changes in depth distribution and spawning timing, as well as resource fluctuations and changes in hydrodynamic connectivity (Edwards and

Richardson, 2004). Hence, while simultaneous and inverse changes between ecosystems indicate an abrupt shift in species’ distributions and abundances (Figure S3), phenological changes could also explain the observed shift in functional structure.

As this functional shift was triggered by a warming phase of a natural climate oscillation, a future cooling phase could potentially act in reverse to our findings, with species shifting southward between ecosystems. Past studies have documented fluctuations in pelagic landings under alternating phases of climate cycles. For example, warm phases of the El Niño are correlated to higher landings of sardine in the North Pacific, while cool phases favor anchovies (Lindegren et al., 2013). However, the North Sea has been identified as a global warming hotspot (Hobday and Pecl, 2014) and fisheries have become increasingly dominated by species with warm temperature preferences (Cheung et al., 2013). Additionally, studies have shown that both the AMO and global warming are influencing sea surface temperatures in the North Atlantic (Ting et al., 2009) and human-induced amplification of the AMO is already evident (Moore et al., 2017). Thus, although a cooling phase of the AMO could slow

(or slightly reverse) warming in these ecosystems, the long-term trends are likely to continue, particularly given the expected increase in marine heat waves in the near future (Frölicher et al., 2018; Oliver et al., 2018).

This functional shift could have major implications for ecosystem functioning, services and governance (Frainer et al., 2017; Pinsky et al., 2018). Declining abundances of pelagic planktivorous species in the EEC likely reduced carbon and nutrient sequestration from the pelagic food web leading to diminished benthic-pelagic coupling (Blanchard et al.,

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2011; Griffiths et al., 2017). Furthermore, a decrease in r-selected species could have shifted the ecosystem from a state of high biomass turnover and rapid nutrient cycling towards an alternative state with slower turnover and higher biomass accumulation (Houk et al., 2017;

McCann et al., 2005; Rooney et al., 2006). This would have a major impact on trophic structure and fisheries productivity, requiring a change in management to account for biomass shifts across trophic levels and reduced surplus production (Houk et al., 2017). By contrast, the SNS shifted toward pelagic dominance, likely having inverse impacts, such as increased input of pelagic-derived energy and connectivity between food webs (Blanchard et al., 2011;

Griffiths et al., 2017). In parallel, this would have shifted the ecosystem toward a state of higher turnover, lower biomass accumulation, and lower overall stability. Yet, increases in overall abundance, particularly in pelagic stocks, could enhance fisheries production (Houk et al., 2017; Rooney et al., 2006). Ultimately this functional shift caused these ecosystems to become more like historical versions of each other, with the SNS resembling the EEC of the early-1990’s and vice versa. Examining historical ecosystem functioning and fisheries patterns in these ecosystems could therefore help guide resource management, which will be crucial for future ocean governance and adaptation capacity (Pinsky et al., 2018).

Climate oscillations and warm extremes are predicted to increase in frequency and severity under ongoing climate change (Cai et al., 2014; Frainer et al., 2017; Frölicher et al.,

2018; Moore et al., 2017). In particular, El Niño events are expected to double in frequency given current trends in greenhouse gas emissions (Cai et al., 2014). Future climate change is therefore likely to cause functional shifts and reorganization of ecosystems with unknown consequences (Aadland, 1993; Frainer et al., 2017). While immediate drastic measures have been recommended to abate human-induced climate change (IPCC, 2014), our results provide insight for pre-emptive conservation planning under current climate projections. Marine resource management, particularly in connected ecosystems with large latitudinal gradients,

176 must prepare for changes in ecosystem functioning and services, and future ocean governance must anticipate such rapid functional shifts (Pinsky et al., 2018).

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5.6. Supplementary material

Supplementary Table 1 | Functional traits used to characterize fish functional structure, related to Figure 2 and 3.

Functional Trait Category Type Units

Length at maturity Life history Numeric Total length (cm)

Age at maturity Life history Numeric Years

Parental care Life history Ordered factor 1 = pelagic egg, 2 = benthic egg, 3 = clutch hider, 4 = clutch guarder, 5 = live bearer

Fecundity Life history Numeric Number of offspring

Offspring size Life history Numeric Total length or diameter (cm)

Trophic guild Trophic ecology Factor Benthivore, benthopiscivore, carcinophage, detritivore, ectoparisite, piscivore, planktivore, scavenger

Trophic level Trophic ecology Numeric Level (unit-less)

Water column position Habitat use Factor Bathydemersal, bathypelagic, benthopelagic, demersal, epipelagic, mesopelagic, pelagic, reef-associated

Substrate preference Habitat use Factor Soft, hard, or open-water

Thermal preference Habitat use Numeric Degrees Celsius

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Supplementary Figure 1 | Temporal changes of fish functional structure in the Eastern English Channel (EEC) and Southern North Sea (SNS), related to Figure 4. Principal component plots showing temporal changes in fish functional structure in the EEC (A) and SNS (B). PCA axes 1 (C, D) were used for Granger causality analyses.

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Supplementary Figure 2 | Multidimensional fish functional in the Eastern English Channel (EEC) (B) and Southern North Sea (SNS), related to Figure 2. (B, C). Points represent each species while polygons represent the functional space (i.e., convex hulls) containing all species in each ecosystem (EEC = light blue; SNS = dark blue; combined = black).

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Supplementary Figure 3 | Kriging-interpolated map of the community-weighted mean abundance of all pelagic species in the Eastern English Channel (EEC) and Southern North Sea (SNS) for the years 1996 – 1997 and 1997 – 1998, related to Figure 2. For this figure the year 1998 in the SNS was combined with 1997 for the EEC, and 1997 in the SNS was combined with 1996 in the EEC to demonstrate the rapid northward shift of pelagic species, and because the SNS survey of a given year actually occurs only four months after the EEC survey of the year before.

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6. Fish communities diverge in species but converge in traits over three

decades of warming6

6In press as McLean M., Mouillot D., Lindegren M., Villéger S., Engelhard G., Murgier J.,

Auber A. Fish communities diverge in species but converge in traits over three decades of warming. Global Change Biology.

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6.1. Preface

In Chapter 5 I documented temporal changes in southern North Sea fish communities at the ecosystem scale, which revealed important changes related to climate variability and ocean warming. However, under the objectives of this thesis several important questions remained unanswered. In particular, the temporal dynamics of fish communities in the northern North

Sea remained uninvestigated, as did spatial patterns in fish community structure for the entire

North Sea. Thus, whether similar environmental responses were occurring in the southern and northern sections of the North Sea and whether similar or different functional traits were responsible for community changes remained unclear. In order to fully describe and understand fish community responses to environmental gradients in the North Sea ecosystem, the overall spatio-temporal patterns of fish functional structure in the entire North Sea needed to be evaluated. Thus, in Chapter 6, I examined the spatial structure of fish communities across the entire North Sea, how spatial variation was related to environmental gradients, and how spatial structuring has changed through time. Furthermore, I compared patterns in taxonomic and functional structure to reveal whether taxonomic and functional changes have been consistent and similar in magnitude and direction and whether fish functional structure has responded similarly or differently to environmental changes in areas with differing species composition.

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6.2. Introduction

Evaluating the spatial and temporal dynamics of fish communities is crucial for understanding and predicting the impacts of global change on marine ecosystem functioning and services

(Frainer et al., 2017; Givan et al., 2018; Holmlund and Hammer, 1999; Worm et al., 2006) as fishes are the dominant vertebrates in marine food webs and strongly influence ecosystem processes such as nutrient cycling, carbon storage, water quality, and habitat maintenance

(Allgeier et al., 2017; Bascompte et al., 2005; Holmlund and Hammer, 1999; Villéger et al.,

2017). In recent decades, ocean warming has profoundly impacted marine fish communities

(Cheung et al., 2013; Flanagan et al., 2019; Fossheim et al., 2015; Wernberg et al., 2016).

Examining spatial and temporal changes in fish communities under warming can provide insight into ecological responses to climate change, and how ecosystem structure may change in the future (Auber et al., 2015; Beukhof et al., 2019; Dencker et al., 2017). Additionally, observational studies can provide robust results for understanding community dynamics and improving predictive models (Mouillot et al., 2013b; Suding et al., 2008). Thus, examining fish community dynamics over the last 20-30 years may be particularly useful for understanding how climate change will affect marine ecosystem structure and functioning.

While marine ecologists have traditionally used species-based (i.e., taxonomic) approaches to assess fish community dynamics, trait-based approaches may better reveal mechanisms structuring communities (Tillin et al., 2006; Bremner et al., 2003). In place of species or taxa, such approaches uses bio-ecological traits that characterize organisms’ responses to environmental change or effects on ecosystem processes (Dıaź and Cabido,

2001; Mouillot et al., 2013b). Over the last few decades, and especially in recent years, trait- based approaches have been shown superior to taxonomic approaches for examining community assembly and ecosystem functioning (Lavorel and Garnier, 2002; Mouillot et al.,

2013b; Pecuchet et al., 2017; Sakschewski et al., 2016; Weiher and Keddy, 1995). Yet,

185 examining changes in taxonomic and trait-based diversity simultaneously may best describe community dynamics (Dencker et al., 2017; Monnet et al., 2014; Villéger et al., 2010). For instance, changes in species composition may not lead to changes in trait diversity if loser species are replaced by species with similar traits (Clare et al., 2015; Villéger et al., 2010;

White et al., 2018). Conversely, declines of a few rare species with unique trait values may have major impacts on trait diversity without notable changes in species composition

(Mouillot et al., 2014, 2013a; Violle et al., 2017).

A variety of studies have documented biotic homogenization in marine and terrestrial communities in response to anthropogenic pressure and climate change (Devictor et al.,

2008a; Magurran et al., 2015; Richardson et al., 2018). In particular, homogenization of species’ traits (i.e., ‘functional homogenization’) is leading to losses in biodiversity and likely ecosystem functioning due to links between organismal traits and ecosystem process

(Devictor et al., 2008a; Olden et al., 2004). For example, in many ecosystems specialist species with unique functional roles are being replaced by generalists more adapted to disturbed environments (Clavel et al., 2011), and losses of such functions could deteriorate ecosystem services (Clavel et al., 2011; Devictor et al., 2008a; Mouillot et al., 2013a).

However, most studies have examined biotic homogenization through species-based approaches, which offer less insight for understanding the mechanisms driving homogenization and the consequences for ecosystem function. Yet, trait homogenization often results from taxonomic homogenization, and the relationship between trait and taxonomic homogenization is not entirely understood (Baiser and Lockwood, 2011; White et al., 2018).

Here, using fisheries monitoring data spanning 33 years, we assessed and compared spatio-temporal patterns of fish taxonomic and trait structure in the North Sea, specifically

186 examining how fish communities were spatially structured throughout the North Sea and how these spatial patterns in taxonomic and trait structure have changed over time.

6.3. Methods

6.3.1. Study area

The North Sea is a continental shelf sea of northwestern Europe that is connected to the North

Atlantic Ocean through the Norwegian Sea in the north, and the English Channel in the South.

The North Sea is a large and relatively shallow marine ecosystem with a surface area of

570,000 km2 and depths generally between 30-50 m in the south and 50-100 m in the north

(Kenny et al., 2009). The ecosystem has a long history of overfishing, particularly during the late 20th century, with notable declines in species such as cod, haddock, and whiting

(Aadland, 1993; Frid et al., 2000; Gislason, 1994). The North Sea is considered a global warming hotspot, with temperatures rising at nearly four times the global average (Hobday and Pecl, 2014), and has experienced considerable climate impacts over the past thirty years, including major changes in fish communities (Montero‐Serra et al., 2014; Simpson et al.,

2011). Recent studies have also documented notable changes in trait diversity, particularly in the southern North Sea (Dencker et al., 2017; Somerfield et al., 2008). However, long-term changes in spatial structure have not been fully examined and whether taxonomic and trait dynamics have followed similar trajectories remains unknown.

6.3.2. Fish community data

Fish community data came from the International Bottom Trawl Survey (IBTS), an annual monitoring campaign that uses stratified random sampling to survey fish communities in 187 approximately 150, 1° longitude × 0.5° latitude survey cells covering the entire North Sea each year in early February. During each survey, a 3-m vertical opening bottom trawl (i.e.,

GOV trawl) with a 10-mm mesh codend is towed for 30 min at an average speed of 4 knots.

In each survey, fishes are identified and counted, and the resulting abundances are standardized to numbers of individuals per km2. IBTS abundance data were obtained from the

International Council for Exploitation of the Sea (ICES). These data were extracted for the period 1983 to 2015 for ICES areas IVa, IVb and IVc, which together encompass the entirety of the North Sea. The final fish abundance dataset included 130 taxa (115 species, 15 identified to genera only) across 154 survey cells over 33 years, for a table containing 130 columns and 4924 rows (some years did not include all 154 survey cells). Prior to all statistical analyses, the entire table of species’ abundances (across all survey cells and years) was log10(x+1) transformed.

6.3.3. Ecological traits

Selecting traits requires careful consideration because universal agreements do not exist on trait number or importance, and trait choice ultimately determines research conclusions

(Lefcheck et al., 2015; Leps et al., 2006; Zhu et al., 2017). Dee et al. (2016) and Mcgill et al.

(2006) stated that trait choice should be based on explicit hypotheses and Botta-Dukát (2005) and Petchey & Gaston (2006) stated that traits should reflect well-defined ecological roles or functions. Studies have shown that the ability to predict community dynamics increases sharply with an increasing number of traits (Frenette‐Dussault et al., 2013; Shipley et al.,

2011); however, Laughlin (2014) showed that predictive power saturates at around eight traits. Here, nine traits related to life history, habitat use, and trophic ecology were collected for each taxa. These included length and age at maturity, fecundity, parental care, offspring

188 size, water column position, thermal preference, trophic level, and trophic guild (Table S1).

Trait data came primarily from FishBase (Froese and Pauly, 2012), Engelhard et al. (2011), and Pecuchet et al. (2017). Traits encompassing life history, habitat use, and trophic ecology were chosen because they are believed to mediate species’ responses to environmental change

(Beukhof et al., 2019; Villéger et al., 2017). For instance, length and age at maturity can mediate species’ environmental responses because they determine growth rate, metabolism, and generation time (Brown et al., 2004; Petchey et al., 2008), Similarly, fecundity, offspring size, and parental care determine population growth, dispersal capacity, and survivorship

(Adams, 1980; Gross and Sargent, 2015; Keck et al., 2014; Lambert, 2008; Ware, 1975;

Winemiller and Rose, 1992). Trophic level and trophic guild determine species’ responses to resource fluctuations and mediate predator-prey interactions and trophic cascades (Duffy et al., 2007; Gravel et al., 2016b; Huxel and McCann, 1998; Schneider et al., 2016; Winemiller and Rose, 1992). Water column position influences species’ distributions, mobility, and habitat requirements (Montero-Serra et al., 2014; Rijnsdorp et al., 2009; Winemiller et al.,

2015), and thermal preference dictates species’ tolerances to temperature changes (Dee et al.,

2016; Givan et al., 2017). This set of traits was chosen to adequately characterize species’ ecological profiles without including redundant or overly-correlated traits, and the maximum correlation among numeric traits was 0.59 (mean = 0.37), well below proposed collinearity thresholds (Dormann et al., 2013). Additionally, missing trait data was not an issue as the final dataset contained 98% of all possible trait values. Lastly, here we used single, fixed trait values for each species, which cannot account for spatial or temporal variation in species’ traits (e.g., fishing-induced size reduction) or trait plasticity; however, the goal of this study was not to examine individual population demographics but rather to examine how interspecific differences in species’ traits explain how and why communities respond to environmental pressures.

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To characterize trait-based community structure, we then calculated community- weighted mean trait values (CWMs) for the entire dataset across all survey cells (143 – 154 per year) and years (33 years). CWMs are calculated as the abundance-weighted mean trait values in the community, which result in single, numeric values for continuous traits, and proportions for categorical traits. Because CWMs as averages, they reflect relative changes in trait structure in the community rather than absolute increases or decreases.

6.3.4. Spatial vs temporal variation

To assess the relative importance of space vs. time in explaining the overall variation in both taxonomic and trait-based community structure, we first applied redundancy analysis (RDA), a multivariate method for examining the variation in a set of response variables (e.g., fish communities) using a set of explanatory variables (Borcard et al., 2011). For taxonomic structure, we performed two separate RDAs with i) survey cells, and ii) years as explanatory factors, and species’ abundances as dependent variables. For trait structure, we applied the same RDAs but with CWM trait values as dependent variables.

6.3.5. Spatial clustering

We then examined whether there were identifiable spatial clusters of taxonomic and trait structure using K-means clustering, a least-squares partitioning method for finding data groupings (Borcard et al., 2011). For both taxonomic and trait structure, we calculated the mean community composition (species’ abundances or CWMs) for each survey cell for the overall time series in order to apply K-means clustering across survey cells and identify distinct spatial zones. For taxonomic structure, we used the Bray-Curtis distance, while for

190 trait structure we used the Euclidean distance. The optimal number of clusters was chosen using the R package NbClust, which integrates multiple indices of cluster optimization.

6.3.6. Ordinations

Next, to examine overall spatio-temporal taxonomic and trait structure and to identify the species and traits most associated with spatial and temporal community variation, we built ordinations of the entire taxonomic and trait structure datasets. For taxonomic community structure we used Principal Coordinates Analysis (PCoA), an ordination method that transforms community data into a multidimensional space that best represents the overall variation of the data (Borcard et al., 2011). PCoA was applied to a Bray-Curtis dissimilarity matrix of species abundance data. This procedure created an ordination of all 4942 communities in the dataset (all survey cells across all years) with the relative positions and distances of communities representing their taxonomic similarity. For trait structure, we first standardized the CWM trait values (by subtracting each value from the mean and dividing by the standard deviation, function scale in R) and then applied Multiple Factor Analysis (MFA), an ordination method that can give equal weighting to all traits. Because categorical traits have several groups (e.g., pelagic, benthopelagic, demersal) they can be given disproportionate weighting by traditional ordination techniques that treat each trait group as an individual entity. Again MFA created an ordination representing the trait similarity of all

4942 communities in the dataset.

6.3.7. Environmental drivers of overall spatio-temporal variation

In order to contextualize fish community dynamics, we next identified the environmental variables with the greatest associations with overall spatio-temporal variation in both

191 taxonomic and trait structure. This was accomplished using RDA with environmental variables as explanatory factors and species’ abundances or CWM trait values as dependent variables. For taxonomic structure, we specifically used distance-based redundancy analysis

(dbRDA) with the Bray-Curtis distance in order to avoid the problem of double zero similarity in species’ abundances (Borcard et al., 2011), while for trait structure we used RDA with the Euclidean distance. Environmental variables included depth, sea surface temperature

(SST), salinity, and bed shear stress (i.e., water velocity over the bottom). Depth was recorded in-situ during fish community surveys. Mean-annual SST data for each survey cell were derived from the Hadley Centre for Climate Prediction and Research’s freely available

HadISST1 database (Rayner et al., 2003). Salinity data came from the NORWegian

ECOlogical Model NORWECOM (Skogen et al., 1995). Bed shear stress data came from a

3D hydrodynamic model developed by Aldridge and Davies (1993).

Because a variety of data were unavailable prior to the late 1990s, we ran additional dbRDA and RDA analyses to identify the primary drivers of spatial community structure. For these analyses we included not only depth, SST, salinity, and bed shear stress, but also chlorophyll-a, suspended sediment, and fishing effort. Chlorophyll-a and suspended sediment were derived from satellite data, and were available from 1998 – 2015 (Gohin, 2011). Fishing effort (number of hours fished) by beam trawls and otter trawls came from (Jennings et al.,

1999a) and was available for the period 1990 – 1995. Because our data incorporated both demersal and pelagic species, we combined beam and otter trawls together into total fishing effort. In order to conduct spatial analyses, for each variable, we calculated the average value per site for the overall time series available. Thus for depth, SST, salinity, and bed shear stress, the value for each site was the mean from 1983 – 2015, for chlorophyll-a and suspended sediment the value was the mean from 1998 – 2015, and for fishing effort the value was the mean from 1990 – 1995. Spatial RDAs were conducted for both taxonomic and trait

192 structure, and forward variable selection (R function ordistep) was used to identify the most parsimonious models, and variable importance was assessed according to adjusted R2 values.

6.3.8. Temporal dynamics of spatial clusters

In order to examine the temporal dynamics of spatial clusters (see section ‘Spatial clustering’ above), for each year we first calculated the centroid (i.e., mean position) of all survey cells for each cluster in the ordination spaces (PCoA for taxonomic, MFA for traits). We then examined the temporal trajectories of each cluster in the ordination spaces and tested whether clusters’ community structures had significantly changed through time using linear regression of each cluster’s first and second axis positions (PCoA and MFA scores) against time. To visualize overall spatio-temporal changes in taxonomic and trait structure, we then generated annual krigging-interpolated maps of PCoA1 and MFA1 scores across the entire North Sea.

Next, to assess whether spatial clusters have become more or less similar through time, for each year we calculated the Euclidean distance between cluster centroids in the ordination spaces (across all n-dimensions) and tested whether or not distances between clusters have increased or decreased using linear regressions against time. To further assess temporal changes in spatial heterogeneity, we also examined annual i) Bray-Curtis taxonomic beta diversity and ii) trait-based beta diversity for the entire North Sea, and tested for changes using linear regressions. Bray-Curtis taxonomic beta diversity was calculated using the R function beta.multi.abund (Baselga, 2017). Trait-based beta diversity was calculated following Chao et al. (2019) using the R function MultiFD.Beta, where beta diversity is a modified version of Rao’s quadratic entropy that gives equal importance to species’ trait differences and species’ abundances.

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In order to identify the species and traits most strongly associated with temporal changes in taxonomic and trait structure in each cluster, we next ran individual cluster-level analyses. For each cluster, we applied partial redundancy analyses (pRDA), a form of RDA

(or dbRDA for taxonomic) that controls for and removes the influence of a confounding variable. We applied pRDA to remove the influence of spatial variation within each cluster and examine only temporal variation, and we extracted species and trait scores along the first axis (the axis associated with time) to assess their contributions to overall temporal changes.

In the pRDA for trait structure, columns in the CWM trait table were weighted using trait weights from the initial MFA analysis so that the individual groups within categorical traits were not treated as individual traits themselves, but rather groups within a single trait (e.g., piscivore and planktivore within trophic guild).

Lastly, to evaluate whether the location or extent of spatial clusters have shifted through time, we applied K-means clustering to the entire taxonomic and trait datasets

(species abundance and CWM tables with 4942 rows) and then subsetted the data for each year and examined changes in the mean latitude and surface area of each cluster through time.

6.4. Results

6.4.1. Spatio-temporal variation in taxonomic and trait structure

Initial RDAs revealed that overall variation in both taxonomic and trait structure was overwhelmingly explained by space, indicating major differences in community structure across sites that may have been maintained through time. Specifically, for taxonomic structure, space explained 24 times more variation than time (56.1% vs. 2.3%), while for trait structure space explained 16 times more variation than time (41% vs. 2.5%). K-means

194 clustering then identified two distinct spatial clusters in both taxonomic and trait structure corresponding to the southern and northern areas of the North Sea (Figure 1). This further indicated that the vast majority of spatio-temporal variation in both taxonomic and trait structure in North Sea fish communities corresponded to a pronounced north-south division.

Figure 1 | Maps and ordination biplots showing overall spatio-temporal variation in taxonomic (a-c) and trait structure (d-f) in North Sea fish communities. Maps show northern and southern spatial clusters in taxonomic (a) and trait (d) structure, biplot vectors show the most contributive species (b) and traits (e), and all sites across all time periods are plotted and colored by spatial clusters (c, f).

The first PCoA axis of taxonomic structure captured 38.8% of the overall spatio- temporal variation, while the first MFA axis of trait structure captured 36.7% of overall variation. The environmental RDAs revealed that these patterns corresponded to a north-south spatial gradient of depth, sea surface temperature, salinity, and bed shear stress, which were

195 all identified as significant explanatory factors (taxonomic dbRDA: F4,3417 = 360, p < 0.001; trait-based RDA: F4,3417 = 362.1, p < 0.001; Figure 2). Taxonomic structure was characterized by higher abundances of Norway pout (Trisopterus esmarkii), haddock (Melanogrammus aeglefinus), long rough dab (Hippoglossoides platessoides), argentine (Argentina spp.), grey gurnard (Eutrigla gurnardus), saithe (Pollachius virens), and mackerel (Scomber scombrus) in the northern North Sea, which is deeper, colder (on average throughout the year), and has higher salinity and lower bed stress, while sprat (Sprattus sprattus), plaice (Pleuronectes platessa), dab (Limanda limanda), herring (Clupea harengus), solenette (Buglossidium luteum), and flounder (Platichthys flesus) were more abundant in the southern North Sea, which is shallower and warmer (on average) with lower salinity from river discharge and higher bed stress from tidal currents (Top 10% of weighted average species scores, n=130 total; Figure 1-2). Overall variation in trait structure was characterized by higher mean length at maturity, trophic level, age at maturity, and fecundity as well as greater abundances of piscivores in the northern North Sea, while pelagics and planktivores were more abundant in the southern North Sea (Top 30% of trait loadings, n=23 total; Figure 1-2).

6.4.2. Spatial drivers of fish community structure

The most parsimonious model of spatial taxonomic structure retained all variables, and variable inflation factors did not exceed six (VIF threshold 10; see Borcard et al., 2011)

(dbRDA: F7,123 = 31.7, p < 0.001; Figure S3). Depth was by far the most important variable in

2 2 explaining spatial community structure (R adj = 0.47), followed by SST (R adj = 0.40),

2 2 2 chlorophyll-a (R adj = 0.39), salinity (R adj = 0.25), bed shear stress (R adj = 0.19), suspended

2 2 sediment (R adj = 0.11), and lastly fishing effort (R adj = 0.07). Chlorophyll-a and suspended sediment were substantially higher in the southern North Sea, while fishing pressure was highest in the southern North Sea and along the western coast (i.e., United Kingdom), with 196 relatively little fishing effort in the deep, northern North Sea (see Jennings et al., (1999a)).

Similarly, the most parsimonious model of trait structure included all variables except suspended sediment (RDA: F6,124 = 21.4, p < 0.001; Figure S3). Again depth had the highest

2 2 2 contribution (R adj = 0.32), followed by chlorophyll-a (R adj = 0.28), SST (R adj = 0.27), salinity

2 2 2 (R adj = 0.19), bed shear stress (R adj = 0.15), and lastly fishing effort (R adj = 0.06). Thus, fish communities in the northern North Sea were associated with not only deeper, colder waters but also lower primary productivity and lower fishing effort, while communities in the southern North Sea were associated with shallower, warmer waters with higher primary productivity and greater fishing effort. Lastly, to examine the potential influence of aggregating total fishing effort, we additionally re-ran spatial analyses with beam trawls and otter trawls separately, and these analyses provided the same results as total fishing effort.

Figure 2 | Ordination biplots showing relationships between environmental variables and overall spatio-temporal variation in taxonomic (a-d) and trait structure (e-h) in North Sea fish communities. Adjusted R2 values from redundancy analyses are indicated for each variable in the corner of each plot.

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6.4.3. Temporal changes in taxonomic and trait structure

Taxonomically, we found that both the southern and northern North Sea changed significantly in species composition over time, as the southern cluster shifted along both PCoA1 and

PCoA2 (PCoA1: F1,31 = 73.3, p < 0.001; PCoA2: F1,31 = 32.4, p < 0.001), and the northern cluster shifted along PCoA2 (F1,31 = 290.4, p = <0.001; Figure 3). These changes were also evident for ecological traits, as both the southern and northern cluster shifted significantly in trait composition along both MFA1 and MFA2 over time (southern MFA1: F1,31 = 97.8, p <

0.001, southern MFA2: F1,31 = 18.3, p < 0.001; northern MFA1: F1,31 = 281.1, p < 0.001, northern MFA2: F1,31 = 50.4, p < 0.001; Figure 3). However, the taxonomic clusters appeared to move further apart along PCoA1, indicating divergence in community structure between the southern and northern areas, whereas trait-based clusters seemed to converge, potentially becoming more similar through time (Figure 3). Kriging-interpolated maps of PCoA and

MFA scores then clearly highlighted the taxonomic divergence and trait convergence of fish communities, as the southern and northern North Sea became progressively more dissimilar taxonomically, but both areas shifted toward similar trait values (MFA scores) (Figure 4).

Examining temporal trends in Euclidean distances between cluster centroids in ordination spaces then confirmed that taxonomic clusters became significantly more dissimilar through time (F1,31 = 19.3, p < 0.001), while trait-based clusters became significantly more similar

(F1,31 = 4.8, p < 0.05; Figure 5). Additionally, by examining temporal trends in taxonomic and trait-based beta diversity for the entire North Sea, we found that the overall ecosystem has become more dissimilar in species structure, but more similar in trait structure (Bray-Curtis beta: F1,31 = 56.14, p < 0.001; Rao beta: F1,31 = 6.6, p < 0.05; Figure 5).

Interestingly, examining cluster movements alongside environmental variables further indicated that despite taxonomic divergence, both trait-based clusters shifted towards communities more associated with higher SSTs (Figures 1-3). Species scores from individual

198 cluster analyses (partial dbRDAs) then showed that the southern North Sea became progressively more characterized by Buglossidium luteum, Sprattus sprattus, Eutrigla gurnardus, Arnoglossus spp., and Engraulis encrasicolus, and less characterized by

Trisopterus minutus and Gadus morhua, while the northern North Sea became more characterized by Eutrigla gurnardus, Trachurus trachurus, Merluccius merluccius, Argentina spp., Trisopterus minutus, and Callionymus spp., and less characterized by Melanogrammus aeglefinus (Top 5% of species scores, Figure S1). However, both clusters shifted primarily toward the same traits, becoming progressively more characterized by higher thermal preferences, pelagic water-column position, lower length at maturity, lower trophic level, and lower fecundity. Additionally, the southern North Sea became less characterized by piscivores and species with pelagic eggs, while the northern North Sea became more characterized by lower age at maturity and less characterized by benthopelagic water-column position (Top

30% of trait loadings, Figure S2).

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Figure 3 | Temporal trajectories of southern and northern taxonomic (a) and trait-based (b) clusters in the PCoA and MFA ordination spaces. Cluster positions each year (circles) are shown along the first 2 axes of ordination spaces and are colored by a temporal gradient from 1983 to 2015.

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Figure 4 | Temporal krigging-interpolated maps of taxonomic PCoA1 scores (a) and trait- based MFA1 scores (b) showing taxonomic divergence and trait convergence of North Sea fish communities.

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Figure 5 | Temporal changes in spatial dissimilarity in taxonomic and trait structure of North Sea fish communities. Panels a and b show temporal changes in the Euclidean distance between cluster centroids in the ordination spaces (PCoA for taxonomic, MFA for traits). Panels c and d show temporal changes in taxonomic (Bray-Curtis) and trait (Rao’s Quadratic Entropy) beta diversity for the entire North Sea. Dotted lines indicate best fits from linear regressions and R2 values are indicated in the upper-left corner of each plot.

6.4.4. Changes in the location and extent of spatial clusters

Although both taxonomic clusters exhibited changes in community structure over time, only the northern cluster changed significantly in mean latitude, moving, on average, slightly northward (F1,31 = 46, p < 0.001). However, the southern cluster expanded significantly in

202 surface area over time (F1,31 = 35.6, p < 0.001), while the northern cluster retracted slightly

(F1,31 = 6.6, p < 0.05). For trait structure, both the southern and northern cluster shifted significantly northward over time (southern: F1,31 = 35.4, p < 0.001; northern: F1,31 = 82.5, p <

0.001), leading to a marked expansion of the southern cluster (F1,31 = 103.7, p < 0.001), and a reduction of the northern cluster (F1,31 = 72.3, p < 0.001). While both taxonomic and trait- based clusters shifted in spatial location and extent over time, the trait changes were much greater, as the surface area of the northern cluster was originally two times larger than the southern cluster, but became slightly smaller than the southern cluster by the end of the time series (Figure 4; Figure S4; Figure S5).

6.5. Discussion

Here, using long-term, spatially-resolved survey data, we documented strikingly similar spatial patterns in taxonomic and trait structure in North Sea fish communities, with distinct southern and northern clusters; however, the temporal dynamics of these clusters differed substantially. We found that the southern and northern North Sea converged toward similar trait structures despite diverging in species composition through time. Additionally, as the southern and northern areas became homogenized in ecological traits, the southern area expanded northward, significantly altering spatial structure. These results have important implications, demonstrating that communities may respond to environmental change by converging toward trait structures better-suited to novel environments. Hence, fish communities under similar environmental pressures appear capable of converging toward similar trait structures even through changes in entirely different species. While such results have been observed in experimental plant communities (Fukami et al., 2005), ours is a novel

203 example showing temporal trait convergence in marine fish communities driven by taxonomically different species.

As a global warming hotspot (Hobday and Pecl, 2014), the North Sea has been heavily impacted by temperature rise over the last few decades, with documented increases in fishes with small body sizes and fast life history strategies, particularly pelagic species (Beukhof et al., 2019; Pecuchet et al., 2017; Simpson et al., 2011). Previous studies have suggested that such species can rapidly track environmental changes due to short generation times, fast population turnover, and by producing small, pelagic larvae with high dispersal rates

(Rijnsdorp et al., 2009). These species are further believed to have greater long-term adaptive and evolutionary capacity given their quick population turnover and greater potential for rapid natural selection (Rijnsdorp et al., 2009). Beyond changes in life history traits, we also found that communities converged toward species with higher thermal preferences. Our results therefore suggest that North Sea fish communities are converging toward species’ with traits more adapted to warmer conditions (Buisson et al., 2013; Fukami et al., 2005; Molinos et al.,

2016). Previous studies have suggested that as natural selection acts on species’ traits associated with fitness, environmental filters should push communities toward similar traits if those traits are well-adapted for environmental conditions, irrespective of the species carrying them (Fukami et al., 2005; Weiher and Keddy, 1995; Winemiller et al., 2015). Additionally, modelling-based studies have suggested that trait convergence and trait homogenization are likely under climate change, as trait diversity may diminish through declines in specialists and increases in generalists (Buisson et al., 2013). Our results therefore support prior literature hypothesizing trait convergence in communities exposed to climate warming.

Taxonomically, we found that the northern North Sea became progressively more

‘north-like’ while the south became progressively more ‘south-like’, leading to overall divergence in community structure. This result suggests that taxonomic changes were driven

204 not only by increases in existing populations, but also by influxes of fishes through the North

Atlantic (around Scotland) and English Channel. While both the north and south became increasingly characterized by small, fast-growing pelagics with higher thermal preferences, this was driven more so by horse mackerel and argentine in the north and by sprat and anchovy in the south. Previous studies have demonstrated major increases in horse mackerel in the northern North Sea in association with climate cycles and increases in chlorophyll a

(Reid et al., 2001). Beare et al. (2004) additionally documented a long-term influx of species with warm southerly affinities coming from over the top of Scotland. In the southern North

Sea, McLean et al. (Chapter 5) documented a northward shift of small pelagic fishes from the eastern English Channel, while Hiddink and Ter Hofstede (2008) observed increasing species richness as warm southerly communities shifted northward over time. Edwards et al. (1999) also documented an inflow of oceanic waters and plankton into both the southern and northern North Sea in the late 1990s that likely facilitated the influx of small pelagic fishes.

Thus, as the ecosystem has changed and environmental conditions have become more favorable for small, rapidly-growing fishes with higher thermal preferences, different

‘winner’ species have likely shifted into the North Sea through both the northerly and southerly openings, leading to increased taxonomic divergence through time.

This study could not properly examine the potential impact of fishing pressure on spatio-temporal patterns in taxonomic and trait structure, owing to a lack of long-term spatially-resolved fishing data. However, we were able to examine the spatial relationship between fish community structure and fishing effort, using temporally-averaged data from available timeframes. We found that distinct spatial differences in fish community structure between the northern and southern North Sea were also reflected in fishing effort, which was more concentrated in the shallow, productive southern North Sea (Jennings et al. (1999a))

Pecuchet et al. (2018) previously showed that fishing effort was related to the distribution of

205 fish traits in the North Sea; however, the authors concluded that fishing effort likely followed spatial patterns in fish communities, as targeted and non-targeted species differ in trait composition. Our results support this hypothesis, as higher fishing effort in the southern North

Sea likely reflects both higher accessibility, because the southern North Sea has a greater coastline and is substantially shallower than the north, as well as higher year-round primary productivity. Yet, it should be acknowledged that the data used here were limited to a five- year time period and may not represent spatial trends in fishing pressure for the overall time series, particularly as overall fishing effort has decreased in recent decades (ICES, 2018).

Beukhof et al. (2019) also examined spatial and temporal patterns in life history traits and concluded that temporal changes in certain traits, notably body size, were likely influenced by fishing pressure. Thus, although it appears that fishing effort followed accessibility and differences in biological production, historical fishing pressure on large demersal fishes may have reinforced the increase in small, rapidly-growing fishes in recent decades. Additionally, although mean thermal preferences have increased throughout the North Sea as the ecosystem has warmed, the increase in small, opportunistic species also suggests heavy exploitation has selected for more fishing-resilient species over time. Assessing the long-term impacts of fishing pressure in different areas of the North Sea would be a valuable extension of this study, and future studies on trait structure should aim to disentangle the relative contributions of warming vs. fishing, particularly for informing models.

Previous studies examining taxonomic and trait-based changes in terrestrial communities have found that biotic homogenization often results from land-use (e.g., farming, road building), because wide-spread generalists can replace specialists following disturbance

(Newbold et al., 2018). In marine systems, intense bottom-trawling and coastal development can degrade or eliminate important habitats that some species depend on, particularly demersal species (De Groot, 1984; Sguotti et al., 2016). In the southern North Sea, important

206 habitats like oyster beds have been replaced by sand and gravel over time due to chronic human impacts (Callaway et al., 2002; Houziaux et al., 2011). Thus, habitat loss and degradation throughout the North Sea could have also contributed to trait homogenization by reducing the relative abundance of specialist species and long-lived demersal species and by favoring pelagic species with fewer habitat requirements.

Our findings provide insight for sustainable development that should be integrated into predictive models estimating changes in community structure under climate change, as well as fisheries and ecosystem-based management strategies. For example, combining data on trait- environment relationships with habitat models may reveal how communities will unfold in the future, with potential implications for ecosystem functioning (Buisson et al., 2013). Future studies should therefore attempt to identify consistent trait-environment relationships in combination with habitat modelling to predict future changes in trait structure across regions with different species pools. With the emergence of trait-based approaches, fisheries and ecosystem-based management should also move toward maintaining and managing the trait structure of communities rather than on a species-by-species basis (Cadotte et al., 2011; Dee et al., 2016; D’agata et al., 2016a). Management strategies that incorporate predicted changes in trait structure will be better able to anticipate how communities will respond to exploitation regardless of species composition.

Here, a trait-based approach uncovered important insights into community assembly and environmental filtering that were not possible with a purely species-based approach, and the ability to transcend species and apply findings across systems is an important strength of trait-based ecology (Mcgill et al., 2006; Winemiller et al., 2015); However, while we documented spatial shifts in trait structure over time, we are unable to explain the potential consequences for the overall ecosystem, as studies linking fish traits to ecosystem processes and functions are currently lacking (Villéger et al., 2017). We were also unable to account for

207 ontogenetic shifts and temporal shifts in trait values, as we used single, fixed trait values for each species. For instance, fishing pressure is known to decrease size and age at maturity within species, and species may exhibit regional differences in life history traits. Using single trait values per species does not permit incorporating such demographic information.

Additionally, we integrated multiple traits to examine the overall spatio-temporal dynamics of fish community structure and did not evaluate the dynamics of individual traits; however such approaches are useful for identifying key traits exhibiting the greatest individual changes

(Beukhof et al., 2019; Engelhard et al., 2011; Henriques et al., 2017). Future studies should also examine temporal changes in spatial trait structure over larger geographic scales using multi- and single-trait approaches to identify macro-ecological shifts in trait structure and whether certain traits are shifting or retreating poleward (Frainer et al., 2017). As with all trait-based studies, trait choices ultimately determine research conclusions, and had we used a different set of ecological traits we may have reached different conclusions. Thus, while the findings of this study appear broadly applicable, our results are limited to the set of traits used and to the spatial and temporal scales examined. For instance, because the IBTS is a bottom- trawl survey some authors have chosen to exclude pelagic fishes when examining these data and excluding pelagic fishes would have provided very different findings. Finally, while this study examined and compared spatio-temporal trends in taxonomic and trait-based community structure, we were unable to fully assess the relative importance of different environmental drivers (e.g., climate vs. fishing) due to a lack of spatially-resolved, long-term data.

While more studies are using trait-based approaches, few have compared long-term spatio-temporal patterns between taxonomic and trait structure. Our results show that fish communities are likely to shift toward trait structures associated with emerging environmental conditions, and that this can be achieved through entirely different species pools. As global

208 environmental change continues, trait convergence and homogenization of communities are likely to occur in similar environments worldwide, particularly as communities shift toward trait structures more adapted for warming conditions.

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6.6. Supplementary material

Supplementary Table 1 | Ecological traits used to characterize fish trait structure.

Ecological Trait Category Type Units

Length at maturity Life history Numeric Total length (cm)

Age at maturity Life history Numeric Years

Fecundity Life history Numeric Number of offspring

Parental care Life history Ordered factor Pelagic egg, benthic egg, clutch hider, clutch guarder, live bearer

Offspring size Life history Numeric Total length or diameter (mm)

Trophic level Trophic ecology Numeric Unit-less

Trophic guild Trophic ecology Factor Benthivore, benthopiscivore, carcinophage, detritivore, ectoparisite, piscivore, planktivore, scavenger

Water column position Habitat use Factor Benthopelagic, demersal, pelagic, reef-associated

Thermal preference Habitat use Numeric Degrees Celsius

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Supplementary Figure 1 | Taxonomic partial distance-based redundancy analyses for the a) southern and b) northern North Sea clusters showing species contributions to temporal variation. The most contributive species were assessed according to their scores on the first axis (CPA1).

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Supplementary Figure 2 | Trait-based partial redundancy analyses for the a) southern and b) northern North Sea clusters showing trait contributions to temporal variation. The most contributive traits were assessed according to their scores on the first axis (RDA1).

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Supplementary Figure 3 | Result of spatial redundancy analyses showing spatial environmental drivers of taxonomic (a) and trait structure (b) of North Sea fish communities.

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Supplementary Figure 4 | Annual maps of taxonomic spatial clusters in North Sea fish communities.

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Supplementary Figure 5 | Annual maps of trait-structure spatial clusters in North Sea fish communities.

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7. Deborealization exceeds tropicalization in marine fish community

thermal shifts7

7In preparation as McLean M., Mouillot D., Goberville E., Lindegren M., Engelhard G.,

Maureaud A., Hattab T., Pinsky M., Auber A. Deborealization exceeds tropicalization in marine fish community thermal shifts.

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7.1. Preface

Chapters 2 – 6 of this thesis examined fish functional structure by integrating multiple ecological traits related to life history, trophic ecology, and habitat preference. While I primarily documented major changes in water-column position and life history traits, I also observed consistent increases in the average thermal preference of communities. For example, in Chapter 6, thermal preference emerged as the most temporally contributive trait explaining changes in fish community structure in both the southern and northern North Sea. This finding is in line with recent studies examining the community temperature index (CTI) – the mean thermal preference of communities – in multiple taxa such as fishes, birds, butterflies, and plants. The majority of studies have reported increases in CTI at multiple spatial scales in response to rising temperatures. One particular study documented nearly ubiquitous increases in CTI in marine fisheries worldwide. However, CTI is a community-weighted mean that reflects relative changes in dominance between warm-affinity and cold-affinity species, and therefore does not examine whether CTI changes are driven by increases in warm-affinity species or decreases in cold-affinity species. Answering this question has important implications for understanding and conserving biodiversity, as differential patterns in abundance changes might be linked to environmental gradients. Yet, surprisingly, prior to this thesis, no study had examined this important question. Therefore, in Chapter 7, I examined temporal changes in CTI in fish communities from the North Sea and other ecosystems across the Northern Hemisphere to identify spatial variation in the underlying process (decreases vs. increases in abundance of warm and cold-affinity species) of CTI, and related these patterns to variation in sea surface temperature and depth regimes.

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7.2. Introduction

Community ‘tropicalization’ – an increase in dominance by warm-affinity species – is increasingly prevalent worldwide, as climate warming is leading to rapid changes in species’ distributions and abundances (Cheung et al., 2013; Devictor et al., 2012; Vergés et al., 2014).

For example, increasing dominance by warm-affinity species of birds, butterflies, and alpine plants has been documented at continental scales (Devictor et al., 2012; Gottfried et al., 2012), and an increase in the ‘mean temperature of the catch’ has been reported for commercial fisheries globally (Cheung et al., 2013). Such findings indicate that biological communities are responding to climate warming through shifts in dominance toward species more tolerant of warmer temperatures (Frainer et al., 2017; Wernberg et al., 2016). However, such changes in dominance do not reveal whether community shifts are due to increasing abundances of warm-affinity species or decreasing abundances of cold-affinity species. For instance, two communities could display identical increases in community temperature index (CTI) even if one community gained warm-affinity species, while the other lost cold-affinity species.

Although both communities display equal thermal shifts, the community characterized by decreasing abundances was likely more impacted, with greater losses in ecosystem functioning (García et al., 2018; Hooper et al., 2005; Lefcheck et al., 2015). Understanding whether increases in warm-affinity species or decreases in cold-affinity species are driving thermal shifts in marine communities has major implications for fisheries and ecosystem functioning, as maintaining productivity in the face of climate change will require maintaining sufficient abundance regardless of species composition (Brander, 2007; Sumaila et al., 2011).

While previous studies have demonstrated increases in CTI in both marine and terrestrial communities, few have used standardized monitoring data, and large-scale marine studies have been mostly limited to fisheries landings (Cheung et al., 2013; Fortibuoni et al.,

2015; Liang et al., 2018; Tsikliras and Stergiou, 2014). While fisheries landings offer a

219 practical basis for examining CTI trends, scientific survey data can provide a more robust picture of community changes because standardized protocols are used to survey communities over long time periods, including both commercially targeted and non-targeted species.

In this study, we develop a novel index for identifying the underlying processes driving community thermal shifts, where we consider four potential processes using the following terms:

● ‘tropicalization’ (increasing abundance of warm species);

● ‘deborealization’ (decreasing abundance of cold species);

● ‘borealization’ (increasing abundance of cold species); and

● ‘detropicalization’ (decreasing abundance of warm species).

We apply this approach to fish communities using long-term data from nineteen scientific monitoring surveys of marine ecosystems (comprising 137, 2°x2° survey cells, i.e., sampling sites) across the Northern Hemisphere that have shown differential changes in sea surface temperatures (SST) over the period 1990 – 2015. We identify spatial variation in the underlying processes of CTI changes and examine the potential influences of temperature and depth regimes.

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Figure 1 | The four potential underlying processes driving changes in the community temperature index (CTI). Increases in CTI can be driven either by decreases in cold-affinity species, i.e., ‘deborealization’ (a), or increases in warm-affinity species, i.e., ‘tropicalization’ (b). Decreases in CTI can be driven either by increases in cold-affinity species, i.e., ‘borealization’ (a), or decreases in warm-affinity species, i.e., ‘detropicalization’ (d).

7.3. Materials and Methods

7.3.1. Fish community data

Nineteen bottom-trawl surveys from marine ecosystems across the Northern Hemisphere were used to examine changes in the community temperature index (CTI) of fish communities over a large geographic scale with substantial latitudinal and longitudinal gradients. All surveys used similar sampling protocols, where bottom trawls were towed for an average of 30 minutes and the species composition and abundances of all captured fishes were identified and recorded (see Appendix S1). In order to minimize spatial sampling bias, we used a

221 randomization procedure to standardize sampling effort across all survey zones. We first aggregated all trawl surveys to 2° longitude × 2° latitude spatial grid cells and then built species accumulation for each cell each year using the R function specaccum (package vegan) with 100 random permutations. Using the species accumulation curves, we then estimated the number of trawls needed to obtain 65% of the asymptote of species diversity for each survey cell each year using the Michaelis-Menten model (R function fitspecaccum in vegan). Cells that did not have a sufficient number of hauls to reach 65% of the asymptote were removed.

For each of the remaining cells, we then randomly selected the minimum number of hauls needed to reach 65% of the asymptote each year, and we repeated this procedure 99 times.

We then took the mean of species abundances across the 99 permutations for each cell each year. This procedure ensured that sampling effort was similar across cells and that certain cells did not contain substantially higher sampling effort than others. The length of time series also differed between surveys, and we therefore examined the time period 1990 – 2015, which maximized temporal overlap between campaigns. Each grid cell was then designated as a sampling site, and only sampling sites that were surveyed in at least 1/3 of all years (1990 –

2015), and with maximum sampling gaps of three consecutive years were considered, resulting in a total of 137 sampling sites. All survey abundance data were log10(x+1) transformed before all data analyses.

7.3.2. Community temperature index (CTI)

Community temperature index (CTI) is a commonly used measure of the average thermal preference of a community and reflects the dominance level of warm-affinity or cold-affinity species (Devictor et al., 2008b). The inferred thermal preference for each fish species in this study (1337 total) was first calculated as the median temperature of each species' occurrences

222 across its' global range of observations for which data were available (Figure S1). Rather than

SST or bottom temperature, we used mid-water-column temperature (i.e., from the surface to

200 meters depth) because the surveys included a mixture of demersal and pelagic species.

We used temperature climatologies from the global database WOD 2013 V2

(https://www.nodc.noaa.gov/cgi-bin/OC5/woa13/woa13.pl?parameter=t) with a spatial resolution of ¼°. These climatologies represent average decadal temperatures for 1955-1964,

1965-1974, 1975-1984, 1985-1994, 1995-2004 and 2005-2012 on 40 depth layers. These data have been aggregated vertically by calculating average temperature of the first 200m depth.

Species' occurrences were extracted from several databases including OBIS (https://obis.org/),

GBIF (https://www.gbif.org/), VertNet (http://vertnet.org/) and ecoengine

(https://ecoengine.berkeley.edu/). After removing duplicated occurrence records (based on geographic coordinates and sampling dates) we made a spatiotemporal match-up between temperature climatologies and species occurrences taking into account both the geographic coordinates of occurrences as well as their corresponding decade (to get rid of climate trends over the past 58 years).

7.3.3. Sea surface temperature

For each sampling location, we examined minimum, maximum, and mean-annual SST, as well as annual SST variation and range. SST data for each sampling location were derived from the Hadley Centre for Climate Prediction and Research’s freely available HadISST1 database (Rayner et al., 2003). The HadISST1 database provides global monthly SST on a 1° longitude × 1° latitude spatial grid, and is available for all years since 1870. These data were derived for each monitoring campaign and spatially aggregated to match the 2° longitude × 2° latitude sampling sites.

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7.3.4. Drivers of changes in CTI

We next examined the rate of change in CTI for all 137 sampling sites, where single rates of change were calculated for each sampling site throughout the entire time-period (1990 –

2015). Rate of change was calculated as the slope of the regression between CTI and time (°C decade-1). We then used linear mixed-effects models to identify the drivers of the rate of change in CTI. As predictive variables, we included the rate of change in SST, mean-annual

SST variation, the initial SST regime, and depth, and as a random effect we included survey campaign. The initial SST regime was defined as the mean SST for each site for the period

1980-1989, the ten years prior to the study period.

7.3.5. Underlying Thermal Processes (UTP) Index

The underlying thermal processes (UTP) index is designed to identify whether changes in CTI are primarily driven by changes (increases or decreases) in the abundance of warm-affinity or cold-affinity species. The UTP index is calculated through the following procedure (Figure

S3):

i. For each sampling site each year, the frequency distribution of thermal preferences

across all species within a given community is generated (Figure S3a).

ii. For each pair of consecutive years (year y and year y+1) the density difference δDp

between years is calculated within each thermal category p according to the

following formula (Figure S3a, b):

훿퐷p = 퐷n+1,p − 퐷n,p

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iii. The median thermal preference Me is then calculated, where each thermal category p

is weighted by the density difference between year y and year y+1 (δDp) (Figure

S3b).

iv. UTP is then calculated as the value of Me scaled between -1 and 1 according to the

following formula (Figure S3c):

2(푀푒 − 푎 ) 푈푇푃 = 푚푖푛 − 1 (푎푚푎푥 − 푎푚푖푛)

where amin and amax are the minimum and maximum thermal preferences in the range

of observed thermal preferences during the years y and y+1. When UTP < 0, the

change of CTI between year y and year y+1 is primarily driven by changes in the

abundance of cold-affinity species (whether increasing or decreasing). In contrast,

when UTP > 0, the change between the two consecutive years is primarily driven by

changes in the abundance of warm-affinity species (whether increasing or

decreasing).

v. The mean of all UTP and CTI values are then calculated for each sampling location

(Figure S3).

vi. Signs of UTP (±) are then compared with signs of CTI (±) to determine the

underlying processes (Figure S3d):

Tropicalization: UTP > 0 and CTI > 0

Deborealization: UTP < 0 and CTI > 0

Detropicalization: UTP > 0 and CTI < 0

Borealization: UTP < 0 and CTI < 0

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7.3.6. Drivers of underlying processes

To identify the environmental drivers of the different underlying CTI processes, we used generalized linear mixed-effects models with binomial distributions, where we tested for differences in i) tropicalization and deborealization, and ii) deborealization and tropicalization. We again tested in the influences of the rate of change in SST, mean-annual

SST variation, the initial SST regime, and depth, and survey campaign was included as a random effect.

7.3.7. Sensitivity tests

To determine how including or excluding pelagic species influenced the results, we recalculated the rate of change in CTI after removing all pelagic species. Additionally, to examine the impact of calculating thermal preferences with different water-column zones

(bottom temperature, water-column temperature, and sea surface temperature) we recalculated the rate of change in CTI with using all three zones. We did this for all possible scenarios, hence for i) all species using bottom, water-column, and surface temperature, and for ii) demersal species only with bottom, water-column, and surface temperature. We then examined the correlation in the rate of change of CTI across all six scenarios. Across the six scenarios correlation values ranged from 0.48 – 0.99 with a mean of 0.72 (when not considering duplicate or self-correlation values), indicating that the results were robust to including or excluding pelagic species, and to potential choices in thermal preference calculation (Figure S5).

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7.4. Results

We found that between 1990 and 2015, CTI increased in 80 out of 137 (58.4%) sampling sites across the nineteen surveys, with a mean increase of 0.21±0.14°C decade-1 (mean ± standard deviation). This indicated widespread, but not ubiquitous, shifts toward warm-affinity species

(Figure 2). In contrast, CTI decreased in 57 sites (41.6%), with a mean of -0.09±0.07°C decade-1. Increases in CTI occurred primarily in the Atlantic Ocean along the East Coast of the United States and Canada, the North, Baltic, and Barents Seas, the Scottish North Atlantic, and Gulf of Mexico, while decreases were more prominent along the West Coast of the

United States, the Bering Sea, Gulf of Alaska, and Aleutian Islands (Figure 2). Mean-annual

SST displayed similar patterns, increasing in 82 of 137 (59.9%) sampling sites between 1990 and 2015 with a mean of 0.32±0.17°C decade-1, while decreasing in 55 sites (40.1%) with a mean of -0.21±0.14°C decade-1.

Linear mixed-effects models (LMEs) then revealed that the rate of change in CTI was positively and linearly related to the rate of change in SST (t-value = 3.0, Wald chi-square test: x2 = 9.0, p < 0.01), thus changes in CTI closely tracked changes in SST whether increasing or decreasing. Cross correlation analysis then revealed that for over 70% of sites the optimal time-lag between changes in SST and changes in CTI was only 0–2 years, while the most common optimal time lag across all sites was 1 year (0-year lag: n = 38, 1-year lag: n

= 45, 2-year lag: n = 15, 3-year+ lag n = 39, Figure 2), indicating community thermal shifts rapidly followed SST changes.

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Figure 2 | Rate of change in community temperature index (CTI) (a), and sea surface temperature (SST) (b) across the 137 spatial sampling sites for the time period 1990 – 2015, along with optimal time lags between SST and CTI changes (c).

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We then decomposed CTI into the four potential underlying processes: tropicalization, deborealization, borealization, and detropicalization. Of the 80 sites where CTI increased through time, only 34 (42.5%) were characterized by tropicalization (i.e., increasing warm- affinity species) while 46 (57.5%) were characterized by deborealization (i.e., decreasing cold-affinity species) (Figure 3). Of the 57 sampling sites where CTI decreased, 36 (63.2%) were characterized by borealization (i.e., increasing cold-affinity species) while 21 (36.8%) were characterized by detropicalization (i.e., decreasing warm-affinity species) (Figure 3).

These patterns were highly spatially-structured, as the Gulf of Mexico, Baltic Sea, and North

Sea were primarily characterized by tropicalization, the northern U.S. East Coast, the Barents

Sea, and the Scottish North Atlantic were primarily characterized by deborealization, the U.S.

West Coast, and Bering Sea displayed prevalent borealization, and detropicalization occurred primarily along the southern U.S. East Coast, the southern North Sea, the Gulf of Alaska, and

Aleutian Islands (Figure 3).

Figure 3 | Map of the four underlying processes of changes in the community temperature index (CTI) (a): tropicalization (increase in warm-affinity species), deborealization (decrease in cold-affinity species), detropicalization (decrease in warm-affinity species), and borealization (increase in cold-affinity species).

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We then found that sites undergoing deborealization were significantly deeper and had colder initial SST regimes than sites undergoing tropicalization, however this may have been an artifact of regional differences in survey campaigns. Generalized linear mixed-effects models (GLMEs) that included survey as a random effect did not identify any significant drivers of differences in tropicalization and deborealization (initial SST: z-value = 1.1, Ward chi-square test: x2 = 1.1, p = 0.3; depth: z-value = -1.8, Ward chi-square test: x2 = 3.2, p =

0.07; Figure S4). Similarly, sites undergoing borealization were significantly deeper than sites undergoing detropicalization, and GLMEs did indicate that this relationship was robust to survey effects (depth: z-value = -2.4, Ward chi-square test: x2 = 5.8, p < 0.05; Figure S4). The rate of change in SST did not explain differences between tropicalization and deborealization or between borealization and detropicalization, suggesting that faster warming or cooling did not favor one process over the other. Additionally, there were was no difference in the rate of change in CTI between either set of processes.

7.5. Discussion

Previous studies have documented large-scale changes in CTI but have not unraveled the underlying processes of such community thermal shifts (Cheung et al., 2013; Devictor et al.,

2012; Vergés et al., 2014). Unraveling these patterns has clear implications for understanding potential impacts on ecosystem structure (i.e., whether abundances are increasing or decreasing in response to warming) and for predicting future biodiversity responses. For example, areas where CTI is increasing due to the emigration or mortality of cold-affinity species could experience population crashes under future climate warming, and should be considered conservation priorities (Auber et al., 2017a; Pearce-Higgins et al., 2015;

Stuhldreher et al., 2014). In contrast, communities increasing in CTI due to immigration or

230 population growth of warm-affinity species will likely have increased abundance and productivity, despite changing community composition (Barange et al., 2014; Day et al.,

2018; Hiddink and Ter Hofstede, 2008).

While past studies have documented widespread increases in CTI (Cheung et al.,

2012; Montero-Serra et al., 2014; Simpson et al., 2011; Vergés et al., 2016; Wernberg et al.,

2013), here we found that both increases and decreases in CTI were common, but that these patterns closely tracked temperature changes. In areas where SST increased over the 25-year study period communities primarily shifted toward warm-affinity dominance, whereas communities shifted toward cold-affinity dominance when SST declined, indicating high fidelity between community responses and temperature changes. Despite the long-term global warming trend, SST has decreased in the Pacific in recent decades due to natural, decadal- scale climate cycles and heat redistribution in the global ocean (Kosaka and Xie, 2013; Nieves et al., 2015), a phenomenon referred to as the ‘warming hiatus.’ Overall, it therefore appears that communities are rapidly and closely following changes in SST, whether increasing or decreasing, but that over long temporal scales communities worldwide are likely to shift toward higher thermal preferences.

Beyond overall increases in CTI, past studies have largely discussed community thermal shifts in the context of increasing warm-affinity species, particularly in relation to poleward species shifts (Kortsch et al., 2015; Perry et al., 2005; Pinsky et al., 2013; Virkkala and Lehikoinen, 2014). However, here we observed that the majority of CTI increases were actually driven by decreases in cold-affinity species (i.e., deborealization) rather than increases in warm-affinity species (i.e., tropicalization). This result has major implications for understanding climate impacts on community structure, particularly as tropicalization and deborealization were spatially non-random and were associated with environmental variation across ecosystems. Interestingly, we found that deborealization occurred in deeper areas,

231 while tropicalization occurred in shallower waters, and a variety of processes may explain this observation. Shallow ecosystems have greater annual temperature fluctuations and more well- mixed waters than deeper ecosystems. Cold-affinity species in shallow ecosystems may therefore have wider thermal tolerances than those in deep ecosystems. Consequently, rising temperatures in shallow ecosystems may permit range expansions or recruitment pulses in warm-affinity species before leading to decreases in cold-affinity species. Furthermore, warm-affinity species may have greater difficulty establishing in deeper ecosystems, which have cooler year-round bottom temperatures. Additionally, deeper, cooler bodies of water like the Barents Sea generally have greater oxygen levels and higher primary productivity than shallower ecosystems, and ocean warming in such locations could indirectly impact cold- affinity species through reduced oxygen and resource availability (Breitburg et al., 2018).

Additionally, while tropicalization occurred in shallower sites, tropicalization was primarily observed in the North Sea. While North Sea fishes have shifted to cooler, deeper waters over the last few decades (Dulvy et al., 2008), Rutterford et al. (2015) showed that

North Sea fishes will eventually be constrained by the depth limitations of this shallow, semi- enclosed system. Hence, the influx of warm-affinity species could be out-pacing the decline or emigration of cold-affinity species, as cold-affinity species are limited in their capacity to shift to northerly or deeper locations. In contrast, deborealization was primarily observed along the East coast of the United States and Canada and along the northern coast of Norway, and these ecosystems are situated along deep, open shelves, which could enable cold-affinity to species to seek refuge in cooler, deeper waters during warming episodes. For instance, along the North American East Coast, cold-affinity species may be shifting northward toward

Newfoundland, which is buffered by the cool Labrador Current, and along the coast of

Norway, cold-affinity species may be shifting northward throughout the Barents Sea. If cold-

232 affinity species shifted to deeper, adjacent locations, their abundances could quickly decline in sampling sites, even due to relatively small geographic movements.

While our index identifies the underlying processes of changes in CTI, it does not identify to what degree increases in warm-affinity species are driven by local demographic changes (i.e., recruitment and mortality of local populations) versus immigration from adjacent ecosystems. Increases in warm-affinity species can arise from both processes, and their relative importance is likely to vary across ecosystems. For example, long-term community changes in the North Sea have been largely driven by increases in the relative abundance of native warm-affinity species rather than the immigration of sub-tropical species

(Dencker et al., 2017; Engelhard et al., 2011). In the northern Bering Sea, increases in CTI are largely attributable to an influx of sub-arctic species shifting northward as sea ice has retreated and diminished the artic-sub-arctic cold front (Mueter and Litzow, 2008). In the

Mediterranean, CTI has increased due to both decreases in local cold-water species as well as an influx of non-native, warm-affinity species coming through the Suez Canal (Givan et al.,

2017). Thus, changes in CTI can be driven by both the dynamics of existing populations as well as species’ immigrations, and future research is needed to disentangle the extent to which large-scale community thermal shifts are driven by each mechanism.

As we examined changes in community thermal structure using inferred thermal preferences, our data represent species’ observed thermal niches rather than species’ true thermal optima. Hence we cannot account for potential shifts in species’ thermal preferences due to plasticity or adaptation. Identifying species’ true thermal optima, as well as plasticity, could only be accomplished through repeated experimentation, and future studies should identify the thermal optima of different fish species, particularly commercially-important species likely to be impacted by climate warming. Such data could be used not only to

233 examine changes in CTI, but also to enhance species distribution and habitat models for predicting biodiversity changes under future climate change scenarios.

While our study was limited to nineteen scientific surveys over a 25-year period, our approach is applicable to other ecosystems and taxa worldwide, and will help unravel the underlying processes of community tropicalization at a global scale. Identifying how changes in species’ distributions and abundances are impacting overall diversity and community dynamics will be invaluable for planning future conservation and management efforts

(Brander, 2007; Gaines et al., 2018; Goldenberg et al., 2018; Wernberg et al., 2016). Indeed, areas with net losses of cold-affinity species will require careful regulation, whereas areas gaining warm-affinity species may have increased productivity and exploitation opportunities

(Barange et al., 2014; Cheung et al., 2010; Day et al., 2018; Sumaila et al., 2011).

Overall, we found that increasing CTI was more frequently driven by decreases in cold-affinity species than by increases in warm-affinity species, and that these differences were linked to ecosystem depth. Future studies should also link spatial patterns in the underlying processes of CTI to variability in environmental seasonality, ocean currents, and other abiotic factors likely to be modified by climate change. While past studies have documented shifts in CTI, ours is the first to decompose CTI into underlying processes at a multi-continental scale, which could aid in anticipating future changes in biodiversity under climate change and therefore implementing adapted management strategies.

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7.6. Supplemental Material

Figure S1 | Calculation of inferred thermal preferences defined as the median of the thermal spectrum (c) of mid water-column sea temperatures (b) from the global distribution of species’ occurrences (a). 235

Figure S2 | Comparison of inferred thermal preference data from this study with thermal preference data from (Cheung et al., 2013) for 314 overlapping species.

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Figure S3 | Calculation of the Underlying Thermal Processes (UTP) Index. Thermal preference distributions are first compared between year y and year y+1 for each community (a) and the difference between the two probability densities is calculated (b). The weighted median (m) of thermal preference is then used to calculate UTP, which is scaled between -1 and 1 (b). Each underlying process is then determined by comparing the signs (±) of the mean rate of change in UTP and CTI (d).

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Figure S4 | Relationships between depth and the four underlying processes of CTI changes. a) map of the four underlying processes of CTI changes, b) map showing the depth of the different sampling sites, and c) boxplots showing the mean depth of sites associated with each of the four underlying processes.

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Figure S5 | Sensitivity tests showing the influence of removing pelagic species and in choosing different temperature zones for calculating thermal preferences. Correlations in the rate of change in CTI are shown between all combinations of sea surface temperature, mid water-column temperature, and bottom temperature for all species and demersal species only.

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Appendix S1 | Metadata and sources for the nineteen bottom trawl survey campaigns used in this study.

Survey Area Year Months Gear Duration Source type median Reference (min) AI Aleutian Islands 1991-2014 May – Sept. N/A N/A OceanAdapt NOAA1 BITS Baltic Sea 1991-2015 Jan. – Dec. N/A 30 DATRAS ICES2 EBS East Bering Sea Shelf 1990-2015 May – Aug. N/A N/A OceanAdapt NOAA1 EVHOE Bay of Biscay & Celtic Sea 1997-2015 Oct. – Dec. GOV 30 DATRAS ICES2 FR-CGFS East English Channel 1990-2015 Sept. – Dec. GOV 30 DATRAS ICES2 GMEX Gulf of Mexico 1990-2015 May – Oct. N/A N/A OceanAdapt NOAA1 GOA Gulf of Alaska 1990-2015 May – Sept. N/A N/A OceanAdapt NOAA1 IE-IGFS Ireland Shelf Sea 2003-2015 Sept. – Dec. GOV 30 DATRAS ICES2 NEUS Northeast US 1990-2015 Feb. – Dec. N/A N/A OceanAdapt NOAA1 NIGFS Irish Sea 2006-2015 Feb. – Nov. Rock 22 DATRAS Hopper ICES2 NorBTS Norwegian Sea, Barents Sea 1990-2015 Jan. – Dec. N/A 30 IMR3 NS-IBTS North Sea 1990-2015 Jan. – Dec. GOV 30 DATRAS ICES2 PT-IBTS Portugal Shelf Sea 2002-2014 Sept. – Nov. NCT, 30 DATRAS CAR ICES2 ROCKALL Rockall plateau 1999-2015 Aug. – Sept. GOV 30 DATRAS ICES2 SCS Scotian Shelf 1990-2015 Summer & N/A N/A OceanAdapt Spring DFO1 SEUS Southeast US shelf 1990-2015 Apr. – Nov. N/A N/A OceanAdapt NOAA1 SWC-IBTS Scotland Shelf Sea 1990-2015 Feb. – Dec. GOV 30 DATRAS ICES2 WCANN West US Coast annual 2003-2015 May – Oct. N/A N/A OceanAdapt NOAA1 WCTRI West US Coast Tri-annual 1992-2004 May – Oct. N/A N/A OceanAdapt NOAA1 1https://oceanadapt.rutgers.edu/ 2https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx 3https://www.hi.no/en/hi/forskning/research-data-1

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8. General discussion and perspectives

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8.1. Conclusions

Rapid changes in marine biodiversity and fisheries collapses worldwide, along with urgent concerns over human population growth and climate disruption, clearly demand improved understanding and management of marine ecosystems (Brierley and Kingsford, 2009; Hoegh-

Guldberg and Bruno, 2010; Johnson et al., 2017; Pershing et al., 2015). This requires an intimate understanding of how communities respond to environmental fluctuations and how community dynamics impact ecological processes. Marine fish communities are integral to ecosystem functioning and services (Holmlund and Hammer, 1999; Villéger et al., 2017), and fisheries are the most direct link between human societies and marine ecosystems, yet, fisheries management has struggled to implement ecosystem-based management due to a limited understanding of community dynamics (Leslie and McLeod, 2007). In an effort to improve our understanding of fish community dynamics, marine ecologists have increasingly adopted the trait-based framework that was originally developed by plant ecologists to identify trait-environment relationships and links between community dynamics and ecological processes (Dıaź and Cabido, 2001; Lavorel and Garnier, 2002). However, in order to anticipate how changes in fish communities might impact ecological processes and ecosystem functioning, a truly firm understanding of trait-environment relationships and functional community responses is critical.

In response to this need, the primary objective of this thesis was to examine the long- term dynamics of fish communities across three interconnected ecosystems with different spatial scales, environmental conditions, and levels of biodiversity in order to identify consistent trait-environment relationships and to uncover the mechanisms structuring fish community responses. In chapter 2, I used a trait-based approach to identify the ecological similarities among species that were highly responsive to environmental changes and uncovered trait-environment relationships that provided a more mechanistic understanding of

243 community dynamics in the Eastern English Channel. Chapter 3 then detailed how communities’ initial trait structure and redundancy could further explain community responses by determining the amplitude of community changes. Chapter 4 evaluated long- term community dynamics in the Bay of Somme, which provided similar results as for the overall Eastern English Channel, demonstrating an important link between the two ecosystems and reinforcing the conclusions on community responses and trait-environment relationships. Chapter 5 then provided an important explanation for community changes in the

Eastern Channel and Bay of Somme, demonstrating that species with environmentally- responsive traits have greatly decreased in those two ecosystems but have increased in the southern North Sea, likely tracking environmental conditions as ocean warming caused favorable conditions to shift northward over time. Chapter 6 then added an important contribution and greatly demonstrated the utility of trait-based approaches for understanding community dynamics – despite diverging in species composition through time, the southern and northern areas of the North Sea shifted toward the same ecological traits, becoming more characteristic of warmer environments. Chapter 7 lastly provided an important step forward for understanding community responses to environmental change, in particular warming, by decomposing changes in the community temperature index (CTI), and demonstrating how different underlying processes led to increases or decreases in CTI under different environmental conditions.

Together, the chapters of this PhD thesis consistently demonstrated that fish community responses to environmental gradients are mediated through ecological traits related to life history, habitat preference, and trophic ecology. Habitat preference was the most important factor in explaining community responses to environmental change, as pelagic species were much more responsive than demersal species, likely owing to higher mobility, greater dispersal capacity, and fewer habitat requirements (Montero‐Serra et al., 2014). In the

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North Sea (and other large marine ecosystems), thermal preference also contributed greatly to community responses, as CTI closely tracked changes in sea surface temperatures.

Additionally, consistent with ecological theory and patterns uncovered for other organisms, this thesis found that community responses to environmental change were largely explained by r- vs K-selected life history strategies (Devictor et al., 2012; King and McFarlane, 2003;

Mims and Olden, 2012). Chapter 5 demonstrated that while pelagic species were the most contributive to community changes between the Eastern Channel and southern North Sea, r- selected strategists had greater environmental responses than K-selected strategists, whether pelagic or demersal. Overall, it therefore appeared that the most important factor determining fish community responses to environmental changes in these ecosystems was habitat use, as pelagic fishes had the strongest environmental responses and mean temperature preferences changed markedly; however amongst pelagic or demersal fishes r-selected strategists were the most responsive (Figure 1).

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Figure 1 | Conceptual figure showing hierarchical patterns of trait responsiveness. Community responses to environmental gradients were primarily described by differences in pelagic vs demersal water-column position, as pelagic species were the most responsive. Beyond water-column position, community responses were highly explained by r- vs K- selected life history strategies, and r-strategists were more responsive than K-strategists, whether they were pelagic or demersal.

The main environmental drivers of temporal changes in the functional structure of fish communities identified in this thesis were climate impacts and fishing. While an increase in sea surface temperature associated with a warming phase of the Atlantic Multidecadal

Oscillation (AMO) was identified as the primary driver of fish community dynamics across the three ecosystems, both climate and fishing shaped community structure, and the secondary effects of fishing should not be overlooked. In the early 1990s, the Eastern Channel was heavily dominated by small, short-lived species, in particular small pelagic species (Auber et al., 2015). This reflects the importance of historical fishing pressure in structuring fish communities, as overfishing large, long-lived demersal species likely shifted the community toward dominance by small pelagics (Molfese et al., 2014; Pauly et al., 1998). The ensuing climate-driven decline in these species highlights how overfishing can render fish

246 communities more vulnerable to environmental fluctuations (Hsieh et al., 2006). In the North

Sea, historical overfishing of larger species indirectly released predation pressure on small, short-lived species, which likely enabled their rapid increase (Daan et al., 2005). Temperature rise associated with the AMO appeared to favor small, short-lived species, and given their opportunistic life histories, these species were able to rapidly increase throughout the ecosystem. While no direct fishing impacts were observed in the Bay of Somme, the species that increased in abundance, notably seabass (Dicentrarchus labrax) and turbot

(Scophthalmus maximus), have been less exploited in the Eastern Channel in recent decades, which may have facilitated their increase in the bay. Additionally, if the decrease in small, r- selected species in the Eastern Channel cascaded into the bay through recruitment failure, the historical impacts of overfishing in the Eastern Channel would extend equally to the bay. Yet, overall, given the strong correlation found between climatic changes and fish community dynamics, and the major increase in community temperature index in the North Sea and other ecosystems, this thesis shows that climate change is rapidly impacting marine fish communities and is emerging as a primary driver of fish functional structure (Henson et al.,

2017).

Ultimately, the results of this thesis provide strong support for using trait-based approaches to describe and understand community responses to environmental gradients. By uncovering trait-environment relationships and identifying the ecological traits grouping responsive species, this thesis achieved a more mechanistic understanding of fish community responses to environmental gradients that was not possible through a purely taxonomic approach. In Chapter 2, a greater number of sites had significant changes in community structure when using a trait-based approach than was previously found using a taxonomic approach (Auber et al., 2017b), and Chapter 6 demonstrated that contrasting dynamics between functional structure and taxonomic structure were possible. While previous studies

247 have examined changes in the taxonomic structure of communities in these ecosystems

(Auber et al., 2017a, 2017b, 2015; Beare et al., 2004), a trait-based approach was necessary to unravel the underlying mechanisms driving community dynamics and to identify the ecological characteristics that determined how and why different species responded to environmental changes.

8.2. Implications for ecosystem functioning and management

Chapters 2 and 5 documented major changes in size structure and the relative dominance of pelagic and demersal fishes in the Eastern English Channel and North Sea and discussed the potential consequences for ecosystem functioning. Indeed, such changes are likely to have major impacts on ecological processes like bentho-pelagic coupling, productivity, biomass sequestration and turnover, and ecosystem stability (Baustian et al., 2014; Blanchard et al.,

2011; Houk et al., 2017; Rooney et al., 2006). Changes in functional structure are also likely to modify trophic networks and the distribution of biomass among different trophic levels, which could impact fisheries (Frank et al., 2005; Österblom et al., 2007). Chapter 3 further documented that community sensitivity to environmental perturbations could be determined by trait structure and redundancy, which has important implications for ecosystem resistance and resilience. This finding suggests that community stability in the face of environmental disturbance can be directly determined by biological structure and redundancy, and thus management strategies should aim to maximize resource exploitation without jeopardizing trait structure and redundancy. For example, fisheries efforts could be concentrated on highly redundant ecological roles, or species with lesser contributions to the overall functional structure of the community (D’agata et al., 2016b). Chapter 4 documented important implications for fisheries and ecosystem functioning, not only in these ecosystems, but

248 potentially for similar ecosystems in other geographic regions. The Bay of Somme is an important fish nursery for the Eastern English Channel, and the major collapse in fish abundances likely caused a negative feedback, inhibiting population recovery in the Channel.

Estuaries are important habitats that contribute to the maintenance of large marine ecosystems worldwide and are critical for many commercial fish populations. It is therefore urgent to examine how fish communities are changing in other fish nurseries to determine if future fisheries collapses are likely. Chapter 6 provided perhaps the most important results for fisheries and ecosystem-based management. This chapter showed that, regardless of species divergence, different areas are converging toward ecological traits more associated with warming environments, and this result suggests that fish communities worldwide may homogenize toward generalist traits and traits correlated to high temperatures. Examining communities with a purely taxonomic approach would have overlooked this finding.

Managing fish stocks on a species-by-species basis is therefore inefficient in the face of climate change, and ecosystem-based management must identify the ecological traits that are likely to increase in fish communities and adapt management strategies in anticipation of these changes. Finally, Chapter 7 offered important guidance for anticipating future changes in biodiversity under climate change. If we can firmly identify the environmental drivers determining whether increases in CTI are caused by decreases in cold-affinity species or increases in warm-affinity species, we may be able to anticipate whether increased or decreased fisheries productivity are more likely in different regions.

8.3. Limitations

While this thesis uncovered important patterns for understanding the functional responses of fish communities to environmental gradients, there were many important limitations to this

249 work. The results and conclusions of all chapters are subject to the choice and number of traits used, as well as the spatial and temporal scales. While I followed previous literature in choosing traits, in particular basing trait choices on relevant hypotheses and research objectives, a different set of traits could provide a different picture of fish community responses. For instance, if the initial trait selection did not include traits implicated in fish community responses, this thesis may not have reached firm conclusions. Additionally, while this thesis identified habitat use and life history as the most important traits in explaining fish community responses, it is entirely possible that other traits not included in this work, particularly physiological traits, could better explain fish community responses to environmental gradients.

Trait-based methods remain limited and subjective, and a variety of analytical choices were possible during this thesis. For instance, both RLQ and redundancy analysis can be used to examine the environmental drivers of trait structure, and there is no “best” method for evaluating trait dynamics (Kleyer et al., 2012). Some authors have also argued that traits with log-normal distributions should be transformed prior to data analyses, which can further modify results and conclusions (Májeková et al., 2016). Most chapters in this thesis examined and ranked trait contributions to community dynamics using principal components analysis and linear regression. However, these methods do not consider the dynamics of the individual species driving changes in different traits or functional groups. Classic statistical approaches can lead to misinterpretations of trait or functional group contributions to community dynamics, particularly when few dominant species drive patterns and have opposite trends to other species sharing similar traits. Innovating approaches to overcome these limitations are therefore still needed (see Appendix A). Furthermore, when analyzing the functional structure of communities, one must generally choose between calculating community weighted means

(CWMs) or binning continuous traits into functional groups, and both approaches have

250 advantages and disadvantages. Because categorical traits are converted to proportions when using CWMs, different trait groups may increase in absolute abundance while actually decreasing in relative abundance and the results of CWM approaches must be interpreted carefully. Although CWMs provide a practical tool for examining community dynamics, changes in trait values reflective relative and not absolute changes in community structure, and disentangling the underlying processes driving community changes requires additional effort. For example, CWMs may reveal that average body length has decreased in the community but cannot identify whether this change was driven by decreases in large species or increases in small species. In contrast, examining functional groups requires subjectively choosing the number of groups and the cut-offs for defining them, and interpreting results can be difficult when there is a large number groups. Furthermore, if selected functional groups do not appropriately aggregate species with similar ecological roles and environmental responses, meaningful interpretations may not be possible.

While this thesis found consistent community responses and trait-environment relationships across the three ecosystems, the conclusions are limited to the examined spatial and temporal scales. This thesis found that pelagic species and species with r-selected life history strategies rapidly responded to environmental changes in these ecosystems over the last three decades, decreasing when environmental conditions became unfavorable, and increasing when they became favorable. However, these findings cannot evaluate the plasticity or future adaptability of these species, and whether or not r-selected, pelagic species will adapt and rebound in the Eastern English Channel or for how long such species will continue to increase in the North Sea is uncertain. Additionally, the results may depend on environmental context, and it remains to be confirmed whether similar trait-environmental relationships are observed in other ecosystems.

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The conclusions of this thesis are additionally limited to correlative relationships only and we cannot conclude with absolute certainty to what extent community changes were actually driven by increases in sea surface temperatures, decreases in fishing pressure, or changes in other environmental variables. While the conclusions of this thesis were supported both by the results and by previous studies, other unexamined factors could have explained changes in community structure. For example, while rising temperatures were identified as a primary driver of fish community dynamics in the Eastern Channel, we did not test the influence of ocean currents, particularly the increase in water flow linked to the Atlantic

Multidecadal Oscillation. In many instances this thesis was also limited by data availability, as long-term, spatially-resolved data on some environmental parameters and fishing effort were not available.

Finally, while this thesis aimed to characterize the functional responses of fish communities to environmental change, a potential strength of trait-based approaches is the ability to examine how changes in community structure impact ecological processes and ecosystem functioning. Yet, this aspect of fish ecology remains poorly developed, and the conclusions of this thesis regarding ecosystem functioning remain speculative (Villéger et al.,

2017).

8.4. Future research directions

A great deal of future work is needed to strengthen our understanding of how fish communities respond to environmental gradients and how these changes impact ecosystem functioning and services. The trait-based approach is relatively young in fish community ecology and many analytical developments are needed. Currently, trait-based studies use single trait values for each species, and little focus is given to intraspecific trait variation.

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Thus, trait-based studies are more representative of changes in adult populations and do not consider population demographics. Fishes display important ontogenetic changes throughout their life cycles, including major changes in trait composition, such as diet shifts, trophic level or settlement from pelagic to demersal habitats. Further work is needed to integrate intraspecific variation into trait-based methods, particularly ontogenetic changes and trait plasticity. For instance, future studies could examine trait dynamics within different life stages by considering larvae, juveniles, and adults separately and defining unique trait values for each stage. Studies where traits are measured in-situ could additionally examine the influence of trait plasticity (e.g., fishing-induced changes in length at maturity) by using dynamic trait values that change over time and space.

As trait-based approaches continue to develop, more work will be needed to identify the most appropriate traits and the optimal number of traits for examining fish community dynamics, as well as to better consider all possible combinations of traits rather relying on a single defined set of traits. While the rule of thumb is that selected traits should have relevant links to research questions and hypotheses, a wide range and number of traits are currently used in fish community studies. Emerging approaches have shown that integrating too many traits can blur patterns and lead to biased conclusions (Ifremer unpublished data). For instance, the hierarchy of trait contributions to community responses may be impacted by the number of traits used, particularly when additional traits contain species with contrasting dynamics. If too many traits that do not contribute to community responses are added in numerical analyses, this could also overshadow changes in the truly responsive traits. In contrast, choosing too few of traits could result in excluding the traits that best explain community responses, or to a poor ability to differentiate species. Additionally, in this thesis, multiple traits were usually integrated and the contributions of different traits to community responses were analyzed simultaneously. Future studies should focus more attention to

253 ranking the contributions of individual traits to community dynamics and identifying which individual traits are the most environmentally responsive. In particular, studies should attempt to identify which combinations of traits are the most responsive to environmental change and whether there are detectable ‘winning’ or ‘losing’ combinations under different environmental contexts. For example, while this thesis found that pelagic species and species with r-selected life history strategies were most responsive to climatic changes, a step forward would be to identify which particular combinations of life history traits are the most responsive. For example, it is possible that some pelagic species are only highly responsive when they have certain combinations of life history traits, and this question remains unanswered.

While the trait-based approach is commended for linking changes in communities’ trait structures to tangible ecological processes, such links remain poorly defined for fish communities. Plant ecologists have successfully linked many traits to ecological processes like primary productivity, nutrient turnover, nitrogen fixation, and decomposition (Cadotte,

2017; Mouillot et al., 2011; Poorter and Bongers, 2006), however, ecological processes are exceptionally difficult to measure in marine ecosystems, and data on such processes are rare.

While enough evidence exists to speculate how changes in trait structure in marine fish communities may impact ecosystem functioning (e.g., changes in benthic-pelagic coupling as a consequence of changes in pelagic or demersal dominance), we are currently unable to quantify how changes in trait structure directly influence ecological processes. Future experimental studies are needed to test these links and observational studies will need to correlate fish community dynamics to real measures of ecosystem functioning in natural systems. Bridging the gap between trait structure and ecological processes will be key to anticipating how climate change will impact ecosystem functioning and services.

As marine ecology moves more and more toward trait-based approaches, species’ traits must be integrated into fisheries and ecosystem-based management. The greatest

254 advantage of the trait-based approach, in a management context, is the ability to apply results across different ecosystems with different species pools. This transferability of results could provide a major short-cut for resource management. Rather than dedicating time and resources to identifying optimal management strategies on a species-by-species basis for different ecosystems, scientists and resource managers could identify widely-applicable management strategies by focusing on traits in place of species. Such approaches could consider functional groups as management units, or examine how different fishing scenarios lead to changes in the functional structure of communities.

Perhaps the most valuable follow-up to this thesis would be to integrate the results into future biodiversity assessments under different climate change and fishing scenarios.

Mechanistic models and habitat models already developed to predict changes in species-based community structure should be combined with ecological traits to predict future changes in the trait structure of communities, ecosystem functioning and potentially ecosystem services.

Habitat models could also be developed directly based on trait-environment relationships or habitat suitability of different functional groups rather than species. Such models could then directly predict changes in the trait structure of communities in response to climatic changes and fisheries strategies. As emerging studies link species’ traits to ecological processes, such models could further estimate changes in ecosystem functioning in response to changes in communities’ trait structures, which will facilitate sustainable development under climate change.

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10. Appendix A: Which traits are most responsive to environmental

change: interspecific differences blur trait dynamics in classic

statistical analyses10

10Pending revisions in Marine Biology as McLean M., Mouillot D., Villéger S., Graham

N.A.J., Auber A. Which traits are most responsive to environmental change: interspecific differences blur trait dynamics in classic statistical analyses. 286

10.1. Introduction

Using traits, defined as any bio-ecological feature influencing individual performance in a given environment (Violle et al., 2007), is widely advocated for understanding the links between community dynamics and ecosystem functioning under global change, but this requires understanding how traits respond to disturbances and which trait groups contribute most to ecosystem processes (Dehling et al., 2016; Gross et al., 2017; Lavorel and Garnier,

2002; Mcgill et al., 2006; Suding et al., 2008). More specifically, ranking the respective contribution of different trait groups to community responses can help identify the ecological mechanisms structuring communities (Fort et al., 2014; Pollock et al., 2012; Sakschewski et al., 2016; Weiher and Keddy, 1995). For example, in a marine fish community, if all species increasing in abundance are related by high temperature preferences, we could likely conclude that changes in community structure are primarily driven by ocean warming, and that future increases in warm-adapted species are likely (Cheung et al., 2013). While this response-trait approach is recognized for providing clearer information for planning conservation and management efforts that are applicable across ecosystems and taxa (Dı́az and Cabido, 2001;

Pecuchet et al., 2017; Winemiller et al., 2015), proper interpretations of trait responses are critical.

Currently, descriptive statistics like ordination analyses are used to rank the respective contributions of different traits to temporal and spatial community responses, i.e. which traits are most responsive to environmental changes in time and space (Legendre and Legendre,

2012; Peres-Neto et al., 2003; Pla et al., 2011). In temporal dynamics, principal component analysis (PCA) is used to examine changes in a given community over time by examining the movement of the community along the main principal component axes, while the most responsive traits are inferred by ranking PCA loadings (Legendre and Legendre, 2012; Peres-

Neto et al., 2003; Pla et al., 2011). Additionally, trait responsiveness can be inferred as the

287 slope of the regression between trait abundance and time or an environmental gradient (Jamil et al., 2014; Noordijk et al., 2010). From a purely descriptive standpoint, these methods provide accurate assessments of the traits that explain the highest amount of variation in a dataset, and thus accurately identify the traits with the greatest contributions to community variation in a statistical context. However, using only such descriptive statistics can lead to misinterpretations of how traits respond to environmental changes.

While ordination or regression-based methods might accurately indicate which trait increased or decreased the most over time or space, this finding does not necessarily mean that this trait was the most responsive to environmental changes. For example, if a certain trait emerged as the most responsive because it had the greatest regression slope, this result could be due to a single dominant species while all other species with this trait were unaffected, thus questioning the unequivocal responsiveness of this group. For instance, ‘piscivore’ could be identified as the trait most impacted by an environmental disturbance, yet if only one dominant piscivore species decreased in abundance, while all remaining piscivores were unaffected we have little confidence that piscivores are actually affected by this disturbance.

Even though, under the mass-ratio hypothesis, changes in the traits of the most abundant species should have the highest impact on ecosystem processes owning to their dominance, it does not imply that they are the most representative of how traits in general respond to changing environments (Díaz et al., 2007; Grime, 1998; Mokany et al., 2008). Rather, other traits could be far more responsive to environmental changes, but simply less dominant in the community. Another problematic case arises when a particular trait is shared by only a few species. If these species increase or decrease synchronously in abundance just by chance, standard methods might indicate that the trait is highly responsive while actually being a random signal (Peres-Neto et al., 2017). Instead, when a trait is shared by many similarly

288 affected species, we have greater confidence that this trait contributes strongly to community responses, and is highly responsive to environmental change.

In this note we aim to point out that using standard statistical methods to identify which traits best explain community dynamics or are most responsive to environmental changes can lead to misinterpretations with important consequences for anticipating changes in biodiversity. Such methods do not account for the potential impacts of individual species, particularly dominant species and species with inconsistent responses. Here, we illustrate the issues outlined above with a simulated case study and two real-world examples using a basic index to rank the contribution of different trait groups to community responses to environmental change.

10.2. Materials and Methods

10.2.1. Trait group contributions to community responses

In this study, for simplicity, we considered trait groups, which are defined as groups of species with shared trait attributes (e.g., pelagic, demersal, piscivore, planktivore, schooling, diurnal, oviparous, etc.). While this approach is most relevant for categorical traits, it can be easily extended by grouping continuous traits, which is common in trait-based studies

(D’agata et al., 2016b; Mouillot et al., 2014). However, it should be noted that the issues outlined above apply equally to community-weighted mean approaches, particularly because dominant species can drive changes in average trait values, masking the responses of other species with similar trait values.

Here, we developed a simple index to demonstrate the potential misinterpretations of using standard statistical methods that do not account for interspecific differences. However, it should b be noted that this index is used purely for demonstrative purposes and is not

289 proposed a solution for integrating interspecific differences in trait dynamics. This simple index, hereafter called the trait response (TR) index, ranks trait group contributions to temporal community dynamics. This index thus considers changes in community structure over time and identifies the most responsive trait groups. This index has three complementary criteria:

i. The slope of the change in trait groups over time (i.e., ∆ abundance or biomass yr-1).

ii. Kendall’s coefficient of concordance, a measure of consistency among changes in

trait group member’s abundances (i.e., whether species within a given trait group

display similar dynamics), ranging from 0 to 1 (Legendre, 2005). When the

coefficient is 1, all species display the same type of change; when the coefficient is

0, there is no consistency among species, and the dynamics of the

corresponding trait group are essentially random. Kendall’s coefficient is

calculated by rank-ordering the abundances of each species across years, and

consistency among species’ abundance rankings within each group is computed via

the mean and sum of squared deviations of the rankings (see (Legendre, 2005)).

iii. The number of species within each trait group with the same type of change

(increase vs. decrease) as the overall trait group. This component first adds a

probabilistic aspect, reinforcing that higher numbers of species with consistent

responses reduce the likelihood that trait group dynamics are due to chance alone.

Secondly, it complements Kendall’s concordance, which can be equal for groups

with different numbers of species.

The TR index is then calculated as the absolute value of the product of these three criteria according to the following formula:

푇푅푖 = |푚푖 × 푊푖 × 푛푖|

290 where i is a given trait group, m is the regression slope or PCA loadings of the change in the trait group (i.e., abundance or biomass; Figure 1e) through time, W is Kendall’s coefficient for the trait group, and n is the number of species with the same type of change as the overall trait group (i.e., increase or decrease). This index produces a unit-less value which is used to rank the overall contributions of each trait group to changes in community structure over time, i.e., to identify the most responsive trait groups. The absolute value is used in order to rank trait group responses regardless of whether groups increase or decrease. Higher values of the index correspond to groups with strong responsiveness, while lower values correspond to groups with weak responsiveness due to either low abundance changes or low consistency among species.

10.2.2. Simulated case study

To qualitatively demonstrate the problems outlined in the introduction, we first created artificial datasets of species’ abundances and traits, where we considered changes in the abundance of ten species comprising four trait groups over four years (Figure 1a-d). For this theoretical example, species abundances were specifically (i.e., non-randomly) chosen to highlight the case of a right-skewed community distribution due to many rare and one dominant species, and the potential impact this can have on analytical interpretations. Thus we allocated large decreasing abundances to a single species, and assigned lower abundances to all other species. We furthermore adjusted species’ abundances so that three of the trait groups had low response consistency among species (groups 1, 3, and 4), while one trait group had high consistency (group 2). Temporal dynamics of the trait groups (Figure 1e) were first calculated using the two standard methods – the slope of the abundance of each trait

291 group over time and the PCA loadings of each group. The TR index was then calculated and trait group contribution rankings were compared across the three methods.

Figure 1 | Temporal dynamics of ten artificial species belonging to four different trait groups (a-d), and the resulting dynamics of the trait groups themselves (e). Artificial data were created to highlight the case where a single dominant species drives trait group dynamics (species #5), and where response consistency is low among trait group members (a, c, d).

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10.2.3. Real-world example 1: reef fish responses to coral bleaching

We next examined coral-reef fish dynamics following a mass coral bleaching event, specifically examining which trait groups were most impacted by coral mortality. The

Seychelles Islands experienced wide-spread coral mortality following severe bleaching during the 1998 El Nino event, which led to substantial changes in benthic structure and reef fish community composition (Graham et al., 2015). Fish abundance data were collected at 21 sites around the Seychelles Islands using underwater visual census (UVC) in both 1994 (pre- bleaching) and 2005 (post-bleaching). Abundance data were collected for 129 species, which were assigned to six trait groups according to species’ main diets: predators, invertivores, planktivores, grazing herbivores (grazers), scraping herbivores (scrapers), and corallivores

(Graham et al., 2015). All species abundances were log10(x+1) transformed before analyses.

The TR index was then calculated and trait group rankings were compared with the rankings from the absolute value of slope and PCA loadings.

10.2.4. Real-world example 2: long-term changes in North Sea fish communities

We next applied the TR index to long-term fish community data in the Southern North Sea, again examining which diet groups were most responsive to environmental changes through time. The Southern North Sea has experienced significant community change in the last thirty years due to sea surface warming, with marked increases in warm-adapted species (Cheung et al., 2013; Dulvy et al., 2008; Engelhard et al., 2011). Fish abundance data have been collected annually since 1983 across the entire North Sea during the fisheries monitoring campaign the

International Bottom Trawl Survey (Verin, 1992). Here we included data for the Southern

North Sea (area approximately south of the 50-m depth contour; (Pecuchet et al., 2017) ranging from 1983 to 2015 for 110 species. Species were assigned to five trait groups according to their main diets: piscivores, benthopiscivores, carcinophages (crab-eating),

293 benthivores, and planktivores. All species abundances were log10(x+1) transformed before analyses. We then calculated and compared the TR trait group rankings against rankings of the absolute value of slope and PCA loadings.

10.3. Results and Discussion

10.3.1. Simulated case study

Trait group #4 was ranked as the group with the greatest contribution to temporal community dynamics, i.e. the most responsive trait, by both the slope of trait group abundance and by

PCA loadings (Figure 2). However, further examination revealed that this pattern was driven by the abundance of a single dominant species (#5) (Figure 1d). Using the TR index, however, group #4 dropped from most responsive to second, while group #2 rose from third to first (Figure 2). While group #2 did not have the greatest change in overall abundance, this group included nearly half the species, all of which decreased in abundance (Figure 1b).

These results highlight the potential discrepancy between standard community-level methods and methods that consider interspecific differences. Here, by considering the response of each species within a trait group rather than the total abundance of the group itself, we found that group #2 was much more representative of community responses as all species within this group had the same dynamics (i.e., decreased in abundance).

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Figure 2 | Comparison of slope, PCA loadings, and the TR index for assessing the contributions of individual trait groups to the temporal dynamics of an artificial community of ten species comprising four trait groups.

10.3.2. Reef fish responses to coral bleaching

All six trait groups decreased in abundance between 1994 and 2005 following the wide-spread coral bleaching event. The absolute value of slope ranked corallivores as the trait group with the greatest contribution to community responses (i.e., the most responsive trait), followed closely by invertivores, planktivores, and grazers, while scrapers and predators had weak responses. PCA loadings, on the other hand, ranked invertivores and grazers as the most responsive groups, while corallivores and planktivores had lesser and nearly equal rankings, and predators and scrapers again had weak responses (Figure 3). Using the TR index, corallivores were ranked as the most responsive group, substantially above all other groups in relative importance, while invertivores, grazers, and planktivores all dropped markedly and

295 had similar responses (Figure 3). While both slope and the TR index ranked corallivores as the most responsive trait group, the relative importance of corallivores in comparison to invertivores and planktivores was much higher for the TR index. In contrast, PCA loadings originally ranked invertivores and grazers as the most responsive groups based on their prevalence and dominant abundances; however, following massive loss of live corals, corallivores were clearly most impacted, as all species were similarly impacted despite their lesser abundances (Graham et al., 2007; Pratchett et al., 2008; Richardson et al., 2018).

Figure 3 | Comparison of slope, PCA loadings, and the TR index for assessing the contributions of reef-fish trait groups to community dynamics following mass coral mortality due to coral-bleaching.

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10.3.3. Long-term changes in North Sea fish communities

All five trait groups increased in abundance over time. Using slope and PCA loadings, planktivores were ranked as the trait group contributing most to community responses in the

Southern North Sea, followed closely by benthivores, with piscivores, benthopiscivores, and lastly carcinophages having lower contributions (Figure 4). Using the TR index, planktivores remained the most responsive group; however benthivores dropped substantially, from second to fourth, while benthopiscivores rose from fourth to second (Figure 4). Carcinophages also rose from fifth to third, while piscivores dropped to last. Thus, when considering species- specific responses, benthopiscivores were much more responsive to long-term environmental changes in the Southern North Sea than benthivores, and carcinophages were more responsive than piscivores.

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Figure 4 | Comparison of slope, PCA loadings, and the TR index for assessing the contributions of trait groups to long-term community dynamics in the Southern North Sea.

Our results draw attention to the danger of statistically examining trait dynamics without considering interspecific differences, especially when communities are composed of few dominant and many rare species. Here, we show that using different methods to examine the same trends can lead to markedly different rankings of the trait groups that are most responsive to environmental changes. Incorporating basic concepts like species dominance and response consistency among trait group members lead to different results than standard community-level methods and highlighted the importance of considering species-specific responses when analyzing trait dynamics.

While rarely discussed in current literature, ignoring the potential impacts of individual species responses, notably dominant species and species with inconsistent

298 dynamics, can greatly bias statistical results and remains a prevalent issue in trait-based studies. Numerous studies examining temporal changes in communities’ trait structures use methods such as PCA, redundancy analysis (RDA), or RLQ analysis, which are all heavily influenced by dominant species. Additionally, while we examined trait groups according to categorical traits, applying such methods to community-weighted mean trait values rather than trait groups does not relive the issue, as major changes in trait dominance can be entirely driven by single species (de Bello et al., 2012, 2007; Nickerson et al., 2018). While recent approaches have been developed to identify the contribution of different species to changes in a single community-weighted mean trait (e.g., temperature preference) (Gaüzère et al., 2019;

Princé and Zuckerberg, 2015), this issue remains unresolved for multi-trait approaches. Here, for simplicity, we examined changes in multiple trait groups within the single trait ‘diet,’ however, integrating multiple traits is necessary to fully examine community responses to environmental change (Lefcheck et al., 2015). Studies examining fish community dynamics generally integrate several traits such as habitat use, diet, body size, and reproductive mode

(Frainer et al., 2017; Pecuchet et al., 2018). Identifying the traits that are most responsive to environmental changes (rather than identifying the species that contribute most to changes in a single trait) in such multi-trait studies is substantially more difficult, as multi-trait dynamics are clearly blurred by interspecific differences.

Furthermore, choosing and assigning traits to different species can have a major impact on results depending on the dominance and dynamics of the species. For example, when a dominant species is both a planktivore and a piscivore depending on ontogeny and resource availability, if the species is classified as a piscivore and has major changes in abundance, the overall conclusion will be that piscivores are heavily impacted by disturbance, even though this result was driven by a single opportunistic species. While seemingly

299 intuitive, such issues remain widespread in trait-based studies and their potential consequences are rarely considered.

A central goal of trait-based ecology is to understand how organisms respond to environmental gradients, notably to anticipate future biodiversity changes (Keddy, 1992;

Mcgill et al., 2006; Weiher and Keddy, 1995; Winemiller et al., 2015). As the global environment continues to change due to both human-induced and natural environmental pressures, understanding how different trait groups will respond is critical to planning how we will adapt conservation and management efforts to maintain ecosystem services (Edwards and

Richardson, 2004; Hulme et al., 1999; Poloczanska et al., 2013; Thuiller et al., 2006a;

Vitousek et al., 1997). As the power of trait-based ecology lies in understanding fundamental trait-environment relationships, we must consider ecological implications, like species- specific responses, to a greater extent in statistical methods. The greatest potential danger lies in misidentifying traits that are most responsive to environmental changes, especially for resource management. For instance, in the artificial example, standard methods identified trait group #4 as the most responsive, which could lead to the conclusion that group #4 is the most characteristic of the community response. Thus, resource managers might mistakenly believe that environmental changes most prominently impact communities through decreases in species in group #4, when in fact decreases in species in group #2 are much more representative. By misidentifying trait-environment relationships driven by dominant species, resource managers could be ill-prepared for sudden changes in community structure driven rare species.

In our reef fish example, an ecosystem at the forefront of climatic disturbance

(Graham et al., 2015; Hughes et al., 2018), standard statistical methods (i.e., PCA) could support the conclusion that invertivores are the most responsive trophic group to coral bleaching, leading to potential misallocation of resources, when in reality corallivores are

300 much more responsive and present a more critical management target. In the Southern North

Sea, an ecosystem highly impacted by climate warming (Dulvy et al., 2008; Engelhard et al.,

2011), standard methods would conclude that benthopiscivorous species have been relatively unimportant to community dynamics through time and are thus unresponsive to sea surface warming, when in reality this group has shown consistent, positive responses. These examples highlight how our basic understanding of community responses to climate change can be compromised if we fail to consider the interspecific differences behind trait dynamics.

As our primary objective in this concept paper was not to develop a new method for examining trait dynamics, but to highlight potential issues arising from standard methods, we acknowledge that the index used here is both basic and imperfect, and alternatives with other ecological criteria and different mathematical structures are feasible. We therefore encourage others to propose additional ecological criteria relevant to examining trait dynamics and to develop alternative methods that build on the concepts presented here. Furthermore, with the goal of accurately identifying trait group responses to environmental change, additional approaches focusing on the underlying mechanisms of trait responses will greatly increase our understanding of trait-environment relationships. Laboratory studies examining how different trait groups, and their constituent species, respond to environmental variation like sea-surface warming can more concretely determine which traits are truly most sensitive to environmental changes, and identify the physiological characteristics linking these traits (Messmer et al.,

2017; Ospina and Mora, 2004; Sandblom et al., 2014; Verberk et al., 2016). Such understanding will be critical for anticipating the ecological impacts of global environmental change.

The examples in this concept paper bring to light a specific case in prioritizing trait group contributions, but also draw attention to the larger issue of framing data analyses and interpretations in ecological contexts. While many powerful tools are readily available to

301 contemporary ecologists, the corresponding results are only as good as the interpretations they permit. As trait-based ecology continues to expand, it is important that we consider the ecological contexts of methods and results in order to generate trait-environmental relationships that accurately reflect community dynamics, a critical step for better understanding ecosystem functioning.

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11. Appendix B: Publications arising from this thesis

McLean M, Mouillot D, Lindegren M, Engelhard G, Goberville E, Maureaud A, Auber A. Deborealization exceeds tropicalization in marine fish community thermal shifts. In preperation McLean M, Mouillot D, Villéger S, Graham NAJ, Auber A. Which traits are most responsive to environmental change: interspecific differences blur trait dynamics in classic statistical analyses. Pending revisions in Marine Biology McLean M, Mouillot D, Lindegren M, Engelhard G, Villéger S, Murgier J, Auber A. 2019. Fish communities divergence in species but converge in traits over three decades of warming. Global Change Biology in press McLean M, Auber A, Graham NAJ, Houk P, Villéger S, Thuiller W, Violle C, Wilson SK, Mouillot D. 2019. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities. Global Change Biology in press McLean M, Mouillot D, Gascoaz N, Schlaich I, Auber A. 2019. Functional reorganization of marine fish nurseries under climate warming. Global Change Biology 25:660–674 McLean M, Mouillot D, Auber A. 2018. Ecological and life-history traits explain a climate induced shift in a temperate marine fish community. Marine Ecology Progress Series 606:175–186 McLean M, Mouillot D, Lindegren M, Engelhard G, Villéger S, Marchal P, Brind’Amour A, Auber A. 2018. A climate-driven functional inversion of connected marine ecosystems. Current Biology 28(22):3654–3660

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12. Appendix C: Publications during candidature not arising from this

thesis

McLean M, Auber A, Stuart-Smith R, Villéger S, Edgar G, Mouillot D. Biogeographical functional convergence in reef fishes. In preparation Frelat R, Hidalgo M, Auber A, Fock HO, Garcia C, Lucey SM, McLean M, et al. Size diversity enhances stability in marine fish communities. In review in Current Biology Johnson S, Yalon A, Reyuw B, McLean M, Houk P. Contextualizing the social-ecological outcomes of coral-reef fisheries management. Pending revisions in Biological Conservation McLean M, Lonzarich D. 2017. An investigation of lepidophagy (scale-eating) in Cyprinodon pupfishes on San Salvador Island, Bahamas. Copeia 105(4):626–629 Houk P, Tilfas R, Luckymis M, Nedlic O, Ned B, Cuetos-Bueno J, Mclean M. 2017. An applied framework to assess exploitation and guide management of coral-reef fisheries. Ecosphere 8(3) McLean M, Cuetos-Bueno J, Nedlic O, Luckymis M, Houk P. 2016. Local stressors, resilience, and shifting baselines on coral reefs. PLoS ONE 11(11)

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