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Estimates of mesopelagic fish biomass according to environmental data in
Mediterranean Sea
Par : Morane CLAVEL-HENRY
Soutenu à Rennes, le 11.09.2015
Devant le jury composé de :
Président : Olivier Le Pape
Maître de stage : Villy Christensen (IOF) et Chiara Piroddi (CSM-CSIS)
Enseignant référent : Didier Gascuel et Olivier Le Pape (AGROCAMPUS OUEST)
Autres membres du jury : Sylvain Bonhommeau (IFREMER)
Les analyses et les conclusions de ce travail d'étudiant n'engagent que la responsabilité de son auteur et non celle d’AGROCAMPUS OUEST
AGROCAMPUS OUEST
CFR Angers
CFR Rennes
Année universitaire : 2014-2015
Spécialité : Agronomie
Spécialisation (et option éventuelle) : Halieutique option REA
Mémoire de Fin d'Études
d’Ingénieur de l’Institut Supérieur des Sciences agronomiques,
agroalimentaires, horticoles et du paysage
de Master de l’Institut Supérieur des Sciences agronomiques,
agroalimentaires, horticoles et du paysage
d'un autre établissement (étudiant arrivé en M2)
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Acknowledgement
Saying thanks for the whole internship is not enough to show my gratitude to Villy
Christensen and Chiara Piroddi. I owe them for how they cared for me, their help, their time
and all what they did for me while I was conducting the internship. I discovered a bench of
new methods to achieve goals and could see again how science needs to be rigorous. Villy
and Chiara also help me with the decision I needed to take and which makes my brain crazy.
Sometimes, receiving praises was too much for me and I felt as if I did not deserve it. But
finally, this is really motivating.
I also owe a lot to all the researchers I met at IOF, and with whom I could enjoy talks about
numerous marine topics and get to discuss my own research. Thanks to Daniel, Gabriel,
David and Deng who gave me scientific papers after our talks. I discovered how data could
be managed and understood that there is not a lack of data, but only troubles to exploit them.
I promise to remember all my work life about the “Soup of stones”, that’s famous way to
gather data and methods from nothing at the beginning. I would like to thanks the master
(Catarina, Yaying) and doctorate students (Shannon, Ana, Bias, Roberto, Nicolás), post-docs
researchers and even the French (Marie, Lola) with whom UBC life was a piece of heaven
and which truly let me enjoy the Canadian way of life. With all the seminars, the coffee
breaks, lunches and workshops, I could have a sight of the wonderful field of fisheries and
marine ecosystem. At Vancouver, I spent a delightful time as a visiting student at IOF and
discovered a way to make scientific research that I could not have done in France.
Special thanks to my closest relative who supported me even as I was far away from her by
my own – I hope I didn’t make you too worried. I keep also in mind how grateful I am to my
laptop which did not abandon me, but supported me with all its power.
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Synthèse
La zone mésopélagique est une zone marine dans la colonne d’eau délimitée entre 200 et
1000 m de profondeur. La lumière n’y parvient que faiblement et n’est pas suffisante pour
maintenir la photosynthèse.
Les poissons mésopélagiques – espèces associées à cette couche - sont souvent
caractérisés par une migration verticale nycthémérale (DVM) vers les eaux superficielles
pour se nourrir de planctons. Le jour, ces espèces font parties de la ‘Deep Scattering layer’
(DSL) qui se situe aux alentours de 400-600 m de profondeur en mer Méditerranée. La DSL
est un motif facilement identifiable lors des enquêtes scientifiques par acoustique. Elle est
constituée de plusieurs espèces marines et est ciblée lors des campagnes scientifiques sur
le Mésopélagique.
Les poissons mésopélagiques représentent une biomasse mondiale totale d’un peu moins 1
milliard tonnes d’après un article de Gjøsaeter et Kawaguchi (1980) qui est paru un peu
après que l’Organisation pour l’Alimentation et l’Agriculture (FAO) se soit intéressée aux
ressources commerciales marines encore inexplorées. Cependant, cette ressource reste
faiblement exploitée : les espèces mésopélagiques ont un riche taux d’acides gras qui les
rendent peu commerciales pour la consommation humaine mais intéressantes dans la
production d’huile ou de farine de poisson. La véritable valeur des poissons mésopélagiques
réside dans leur importance au sein des écosystèmes marins. Ils constituent une part
importante des régimes alimentaires de poissons à intérêts commerciaux comme le thon,
mais également des oiseaux, des cétacés et autres mammifères marins. De leur position
trophique dans les écosystèmes marins, ils sont situés à une place clef : leur DVM relie les
zones riches et superficielles des océans avec les zones pauvres et profondes, accélérant,
ainsi, le processus de transport du Carbone. La DVM joue donc un rôle important dans ce
qui s’appelle la pompe biologique du carbone.
La mer Méditerranée, une des mers les plus salines au monde, est un espace physiquement
entouré par les continents européen, asiatique et africain. Elle est un hotspot de biodiversité
malgré une pression par les activités humaines très intense (pêcheries, pollutions et rejets
terrestres, tourisme) et un changement climatique qui affectent les eaux méditerranéennes.
Les dernières décennies ont été marquées par un changement dans les paramètres
environnementaux telles que la salinité ou la température et ceux-ci peuvent avoir perturbé
les biomasses marines présentes. Cette mer représente, à elle seule, ce qui peut se dérouler
dans les océans et constitue donc un exemple adéquat pour comprendre ce qui se passe
dans les eaux marines à petite échelle.
Il est supposé que les poissons mésopélagiques sont sensibles à certains paramètres
environnementaux. Les biomasses estimées en 1980 ont seulement été évaluées à partir
des données de captures et acoustiques, ou encore de la relation avec leurs proies. Estimer
la biomasse des poissons mésopélagiques grâce aux paramètres environnementaux avec
des analyses statistiques n’a pas encore été réalisé. Cette estimation est relativement
importante pour la connaissance des milieux marins et du cycle biochimique du carbone. Elle
peut être aussi intégrée dans la construction de modèles écosystémiques tels qu’Ecopath
with Ecosim. Comme de récentes études ont supposé que la biomasse des mésopélagiques
pouvait être sous-estimées en raison des méthodes utilisées, l’estimation plus précise de la
biomasse représente un intérêt nouveau.
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Nous nous sommes donc demandés quels facteurs environnementaux pouvaient impacter la
biomasse des poissons mésopélagiques et comment cette dernière aurait pu évoluer sur une
période de 30 ans (entre 1980 et 2011). Le cœur de l’étude constituait à établir un modèle
avec deux méthodes utilisant la biomasse observée à partir de la littérature scientifique
établie en mer Méditerranée et des données environnementales issues de modèles
biogéochimiques : la salinité, la température, l’oxygène, la production primaire et la
bathymétrie. Afin de confronter les deux méthodes, nous les avons comparées sur leur
performance et sur les prédictions.
La première méthode, Random Forest, utilise la technique d’apprentissage par ensemble
d’arbres décisionnels. Pour ce faire, la méthode se base sur la prédiction issue de plusieurs
arbres décisionnels après les avoir moyennés. Un arbre décisionnel dans l’algorithme de
Random Forest est construit à partir de plusieurs sous-échantillons issus de l’ensemble
d’apprentissage (les données observées). Les sous-échantillons sont eux-mêmes
déterminés par un sous-ensemble de variables choisies aléatoirement : à un nœud de
l’arbre, un échantillon va être divisé selon la valeur des variables sélectionnées. Cette
méthode est de plus en plus utilisée dans les modèles écologiques et nous avons cherché à
tester sa performance.
La deuxième méthode, le modèle additif généralisé (GAM), réalise une régression sur des
données qui peuvent être non-paramétriques. Elle utilise une fonction de lien, ici la fonction
‘identité’, pour relier la valeur dépendante aux variables environnementales. Des fonctions
non spécifiées sont estimées par des techniques de lissages locaux afin de déterminer la
relation d’une variable environnementale avec la variable à expliquer (la biomasse). En
outre, il est toujours possible d’avoir des relations linéaires entre des variables explicatives et
la variable de réponse.
Ces deux méthodes ont utilisé un jeu de donnée de 905 biomasses éparpillées en mer
Méditerranée. La couverture spatiale seulement localisée à quelques zones (Mer des
Baléares, Mer Egée, Mer Ionienne, Mer d’Alboran) peut avoir limité les résultats.
Les analyses statistiques ont montré qu’en termes de performance, Random Forest était la
meilleure méthode. La variable environnementale qui influençait le plus l’estimation de la
biomasse se trouve être la salinité pour les deux méthodes. GAM a été beaucoup plus
influencé par ce paramètre que Random Forest. Il y eut, entre autre, un phénomène entre
1988 et 1998, où la salinité ayant augmenté autour de la Crête aurait pu provoquer un
accroissement de la biomasse de mésopélagiques dans cette région. Les estimations de RF
durant cette même période n’ont pas été perturbées.
Avec Random Forest, les variables environnementales avaient une relation avec la
biomasse qui correspondait le plus à la littérature : une corrélation en partie positive avec la
production primaire et l’oxygène et une corrélation plutôt négative avec la température. Avec
GAM, quelques incohérences en termes de relation avec la biomasse apparaissaient telle
qu’une corrélation négative entre la biomasse et la production primaire. Nous avons pu
observer, par ailleurs, que la distribution de biomasse en mer Méditerranée avec RF était
sensible à la distribution de la production primaire.
Les deux méthodes estiment également la biomasse des mésopélagiques différemment.
Entre 1980 et 2011, la biomasse des mésopélagiques n’aurait pas été marquée par un
accroissement ou une diminution de la biomasse. Plus précisément, GAM n’a pas de
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tendance qui se dégage. Pour RF, nous pouvons néanmoins observer une augmentation
graduelle de la biomasse jusqu’en 2000 puis une diminution de la biomasse en 2011.
Random Forest serait, en termes de performance et de sensibilité envers certaines
variables, une méthode appropriée pour prédire la biomasse de mésopélagique. Cependant,
la prédiction de biomasse relativement élevée sur les plateaux continentaux (i.e., 0-200 m de
profondeur) remet en question son usage. En outre, le fait qu’il soit difficile d’obtenir des
intervalles de confiances et de prédictions autour des valeurs estimées peut impliquer que
les prédictions varient énormément et ne soient pas fiables. En revanche, GAM donnent des
résultats plus satisfaisants sur les variations de la biomasse autour de la prédiction.
La biomasse estimée pour les 31 années et par les deux méthodes se trouve entre 20 000 t
et 35 000 t pour toute la Méditerranée. Cela correspond à un ordre 100 fois inférieur de
l’estimation de Gjøsaeter et Kawagachi (2,4 millions tonnes). Cette différence d’estimation
peut être attribuée aux méthodes de calcul utilisées pour extraire la biomasse des articles
scientifiques ou alors au fait que les articles scientifiques utilisés ne ciblaient pas
particulièrement les espèces mésopélagiques. Nous avions potentiellement un échantillon
sous-représentant la biomasse de mésopélagiques capturée. Il est prévu de reproduire
l’expérience à une échelle planétaire. Nous pourrions ainsi déterminer l’origine du problème :
si les biomasses sont d’un ordre supérieur, les articles utilisés pour la mer Méditerranée
donneraient des biomasses erronées.
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List of Acronymes
ACE : Alternative Condition Expectation BRT: Boosted Regression Tree C: Carbon CIT: Conditional Inference Tree CV : Coefficient of Variation d: day DMW: Deep Mediterranean Water DSL : Deep Scattering Layer DVM : Diel Vertical migration EMED: Eastern Mediterranean FAO: Food and Agricultural Organisation GAM : Generalized Additive Model GCV: Generalized Cross Validation LIW: Levantine Intermediate Water MAW: Modified Atlantic Water MSE : Mean of squared residuals N: Azote OOB : Out of Bag PPR : Primary Production RF: Random Forest RMSE : Root Mean Square Error WGS: World Geodetic system WMED: Western Mediterranean
vii
List of Figures
Figure 1: Clusters of biogeochemical regions of the mesopelagic layer in Mediterranean Sea.
(Reygondeau et al.,2015) ............................................................................................... 4
Figure 2: Variation of the environmental parameters on the period studied : 1980-2011,
average on the whole Mediterranean Sea. ..................................................................... 6
Figure 3: Distribution of the samples in the Mediterranean Sea and values of the biomass
(t/km²) in each sample. ................................................................................................... 8
Figure 4: Process of random Forest from n trees to get the model. ....................................... 9
Figure 5: ACE transformation of independent variables included in the regression ...............12
Figure 6: Scatter plots of observed versus predicted mesopelagic fish biomass from a)
Random Forest model and b) GAM. The blue dashed line is the 1:1 line, the black line is
the fit. The black dashed line in a) is the fit without the bias correction. .........................15
Figure 7: Partial Dependence Plot from Random Forest model. The y-axis is the absolute
value of the prediction when other variable are fixed. ....................................................16
Figure 8: Predictor variable importance in Random Forest approaches with their confident
interval. .........................................................................................................................17
Figure 9: Form of the smooth functions for the selected covariate. Black solid line is the
smooth function estimate with grey intervals representing the 95% confidence intervals.
......................................................................................................................................18
Figure 10: Boxplot of the RMSE got from a 10 folds cross-validation repeated 100 times. ...19
Figure 11: Distribution of the predicted biomass in 1980 with a) RF and b) GAM .................20
Figure 12: Biomass of mesopelagic fish from 1980 to 2011 estimated by RF (continuous line)
and GAM (dashed line). .................................................................................................21
Figure 13: Variance of the mesopelagic biomass for 31 years: 1980-2011 extract from a) RF
and b) GAM ...................................................................................................................22
Figure 14: Rate of the climate change pressure on Mediterranean Sea according to
Reygondeau et al. (2015) ..............................................................................................25
Figure 15: Spatial distribution of the 7 clusters of the chlorophyll dynamics in Mediterranean
Sea (Sources: D’Ortenzio and Ribera d’Alcalá, 2009). ..................................................27
Figure 16: Cartography of the salinity (on the left side) and the biomass estimated by GAM
(on the right side) for the a) 1988, b) 1993, c)1998 years. .............................................28
List of Tables
Table 1: Main biogeochemical characteristics of the Mediterranean Sea. .............................. 3
Table 2: Environmental predictors used in the analyze of the biomass .................................. 6
Table 3: Parameters contribution to GAM model’s deviance.................................................17
Table 4: GAM significance levels of models terms. ...............................................................18
Table 5: Comparison between the results of the study and the result from Gjøsaeter and
Kawaguchi: ....................................................................................................................26
viii
Table of Contents
Acknowledgement ............................................................................................................................ii Synthèse .......................................................................................................................................... iii List of Acronymes ............................................................................................................................ vi List of Figures .................................................................................................................................. vii List of Tables ................................................................................................................................... vii
1. INTRODUCTION AND CONTEXT ....................................................................................................... 1
2. MATERIAL AND METHODS .............................................................................................................. 2
2.1. Study site: Mediterranean Sea. ............................................................................................... 2
2.1.1.General 3-dimensional geochemical features and the global changes ................................. 3
2.1.2.Biological features: ................................................................................................................. 5
2.2. Environmental predictors ........................................................................................................ 5
2.3. Biomass information ............................................................................................................... 7
2.4. Biomass distribution models ................................................................................................... 8
2.4.1.Random Forest ....................................................................................................................... 8
2.4.2.General non parametric regression model .......................................................................... 11
2.5. Prediction of biomass ............................................................................................................ 14
RESULTS ................................................................................................................................................. 15
2.6. Comparison of model performance ...................................................................................... 15
2.7. Comparison of the estimation of the biomass in Mediterranean Sea .................................. 19
2.7.1.Biomass in 1980, an example ............................................................................................... 19
2.7.2.Temporal biomass estimates ............................................................................................... 20
3. DISCUSSION ................................................................................................................................... 23
3.1. Methodological considerations: validity of the data............................................................. 23
3.1.1.Reliability of the environmental data ................................................................................... 23
3.1.2.Miscalculation of the observed biomass. ............................................................................. 23
3.1.3.Catchability cause. ................................................................................................................ 24
3.2. Model performance .............................................................................................................. 24
3.3. Estimates of biomass. ............................................................................................................ 25
4. CONCLUSIONS AND PERSPECTIVES ............................................................................................... 28
5. BIBLIOGAPHY ................................................................................................................................. 30
Annex I: Maps of the environmental predictors ........................................................................... viii Annex I: Maps of the environmental predictors ........................................................................... viii Annex II : Mesopelagic fish families and species of the Mediterranean Sea .................................. ix Annex III: Distribution of variables from the samples ...................................................................... x Annex IV : Scatterplots of environmental variables and the biomass of samples .......................... xi Annex V: 1) Analyses of GAM residuals and uncertainty of 2) GAM and 3) RF estimates ............. xii Annex VI : Scientific literatures used for the extracting of the biomass. ...................................... xiii
1
1. INTRODUCTION AND CONTEXT
Mesopelagic fish are fish species which spend the day in the mesopelagic zone
between 200 and 1,000 m depth (Gjøsaeter and Kawaguchi, 1980). This layer is itself
defined as disphotic: place where there is enough solar illumination to discern day and night
periods but unable to support photosynthesis. Mesopelagic species are found in all oceans
(Gjøsaeter and Kawaguchi, 1980) with the exception of some species that are endemic to
specific areas (e.g., Electrona antartica (Myctophidae) at South of the Antarctic Polar Front
(Hulley, 1990)). The Myctophidae family is the main one in mesopelagic water and
represents the most abundant family in the ocean (Catul et al., 2011). Few species have a
commercial value due to their high fat acids rates (Lea et al., 2002) used for production of
fishmeal and fish oil (like Benthosema pterotum (Myctophidae) (Valinassab et al., 2007)). In
general, though, fishing for mesopelagic fish is not economically viable yet.
A diel vertical migration (DVM) occurs for most of those species. Some come to shallow
water during nighttime for trophic purposes and migrate to depth before daytime where they
are less active. This concentration of fish forms several layers in the water column where
one, from 400 m to 600 m depth (Olivar et al., 2010), is famously called the deep scattering
layers (DSL). The other part does not migrate at all, like the genus Cyclothone
(Gonostomatidae), or just towards some intermediate depth layers (Legand et al., 1972). The
DVM is hardly perturbed by seasonal variation (Pearcy and Laurs, 1965), but do show higher
biomasses in the summer than in winter (Pearcy et al., 1976). Whereas vertical migration is
frequent, horizontal migration is seldom observed (Gjøsaeter and Kawaguchi (1980), Reid et
al., 1991).
Mesopelagic fishes are particularly important for their trophic role in the food web. Their
preys consist on zooplankton (William et al., 2011) and they are main prey in the diet of
several top predators such as penguins, cetaceans (Cherel et al., 2008) and seabirds
(Barrett et al., 2002) but also in the diet of commercial valuable species (Würtz, 2010) such
as the scombrids (Allain 2005, Ménard et al., 2000). With a trophic level ranging between 2.9
and 4 (Valls et al., 2014), they are, therefore, an important vector between low trophic level
species and higher trophic level species. Mesopelagic fishes are also recognized as an
important component of the ‘biological pump’ of carbon into the deep water. Because of their
vertical migration and/or their feeding of migrant zooplankton, they provide, indeed, an active
transport of organic carbon below the depth of carbon’s remineralization (Davison, 2011;
Irigoien et al., 2014). In the Northeast Pacific, it was estimated that mesopelagic fishes can
export 23.9 mg.C.m-2.d-1 into deep layers (Davison et al., 2013). The organic carbon
components from their metabolism are then used by microbial fauna (Irigoien et al., 2014).
Thus, estimating their biomass can improve the knowledge of the biogeochemical cycle of
oceans (Irigoien et al., 2014) and be useful for the construction of ecosystem models (e.g.,
Ecopath with Ecosim (Christensen et al., 2009)).
According to several studies, mesopelagic fishes biomass dominates the global fish biomass
(Mann, 1984). Gjøsaeter and Kawaguchi (1980), in a study conducted for the Food and
Agricultural Organization of the United Nations (FAO) on unexplored commercial resources,
estimated a biomass of approximately 1,000 millions tons. However, several studies as the
ones from Irigoien et al. (2014) and Kaarvedt et al. (2012), suggest that this estimate is a
significant underestimate because of the methods used to estimate the biomass.
2
For example, in the first biomass estimates of mesopelagic fish, Gjøsaeter and Kawaguchi
based their analyses on catch from sampling nets (commercial trawl, micronekton net),
acoustic and eggs and larva surveys to compute the abundance per area. Since some
species have the ability to avoid nets or escape during trawl survey (Kaartvedt et al., 2014;
Mann, 1984) and some do not have swim bladders, such methods could not be enough to
assess their current biomass in ocean. Moreover, at the time the study was conducted,
acoustic techniques had an inefficient level of knowledge on the target strength of
mesopelagic fish. This parameter based on swim bladder features is an important tool for a
reliable interpretation of acoustic data. Tseitlin also evaluated the mesopelagic species
biomass. He, first, estimated the biomass of mesoplankton and then made a correlation
between the mesoplankton and the mesopelagic fish biomass (Evgeny Pakhomov, Personal
communication). His estimates was slightly higher than Gjøaseter and Kawaguchi’s biomass
but did not reach the 10-times factor supposed by Irigoien et al. (2014)
Lastly, as the relationship between mesopelagic fish and its environment was demonstrated
(Norheim, 2014), their biomass may have changed due to global warming and stronger
pressure from human activities. Furthermore, a need of species distribution models in
Mediterranean Sea is required to understand the effects of changing environmental
conditions (The MerMex Group, 2011). Thus this report aims at estimating mesopelagic fish
biomass exploring different approaches than the ones previously utilized. In particular, using
the Mediterranean as case study, statistical analyses estimate biomass as a dependent
variable of physical, chemical and biological parameters. The overall objective is to identify
strong and reliable statistical approaches as a first step toward a global implementation. In
this report, we assessed two different statistical methods: a regression tree and a traditional
classic regression. The first, also called random forest, is a tool that is increasingly used in
marine ecological research because of its robustness and the user-friendly implementation of
the algorithm with R Packages. The second, Generalized Additive Model, is a tool frequently
used for ecological modelling. The main question of this study was: which of the random
forest model and generalized additive model is the most proper tool to estimate the
mesopelagic fish biomass taking into account environmental parameters and time?
The outcomes could indeed help understanding better the dynamic of mesopelagic fishes
accordingly to environmental parameters and evaluating spatial temporal distribution of those
fish. This report has a first part explaining the relationship between the mesopelagic fish
biomass and the environmental parameters. The second part gives the estimates of biomass
on a time-series between 1980 and 2011 and the distribution of the mesopelagic fish
biomass in Mediterranean Sea.
2. MATERIAL AND METHODS
2.1. Study site: Mediterranean Sea.
With a surface of approximately 2 510 000 km² and an average depth of 1500 m, the
Mediterranean Sea is considered a semi-enclosed sea due to the small exchange of water
with the other oceans/seas (Atlantic Ocean via Gilbraltar’s strait, Black Sea via the Bosporus
and the Dardanelles and the Red Sea with the Suez Canal). These features make the basin
the world’s largest, deepest and most highly pressured enclosed sea.
3
The Mediterranean Sea has two basins geographically distinct in terms of circulations and
water masses connected by the Strait of Sicily. Those nearly equal size basins are divided in
several basins characterized by the biogeochemistry of the water (e.g., Ionian Sea, Adriatic
Sea, Ligurian sea, etc…). The general circulation of water masses follows an anticlockwise
flow along the continental slope but each basin has an intern independent circulation.
Through the both basins, literatures differentiate three layers of water masses: the Modified
Atlantic Water (MAW), the Levantine Intermediate Water (LIW) and the Deep Mediterranean
Water (DMW) which are respectively around 0-150 m, 150-400m and below 400 m depth
(Zavatarelli and Melior, 1995). Each of these layers has slightly different characteristics of the
water masses which explain their formation (Lascaratos et al., 1999). The deep-sea oceanic
domain, which interested us- the Bathyl zone - covers 72 % of the Mediterranean Sea (Emig
and Geistdoerfer, 2004).
The Mediterranean Sea is as a region considered to be strongly affected by the global
warming. All patterns from the ongoing warming in the world’s ocean occur at a lower scale
in Mediterranean Sea (Lejeusne et al., 2009).
2.1.1. General 3-dimensional geochemical features and the global changes
Geochemical features of Mediterranean Sea are issue of many scientific articles. The semi-
closed sea can be characterized per each basin and layer of the water column (Table 1).
Table 1: Main biogeochemical characteristics of the Mediterranean Sea.
Parameters General values by basins (WMED/EMED) in the water column (Exception with the primary production)
Values associated with the observed biomass used for the training of the model.
Salinity 38.4 / 39.1 in LIW 38,4 / 38,7 in MDW (Zavatarelli and Melior, 1995)
Mean: 38.2 Min / Max: 37.2 / 39.1
Temperature 13.0 / 15.5 °C in LIW 12.7 / 13.6 °C in MDW (Zavatarelli and Melior, 1995)
Mean:16.1 °C Min/Max: 14.4 /20.6 °C
Primary Production
20 / 25 g C/m²/year (Danovaro et al., 2001) 0.3 / 0.6 g C/m²/day WMED (Bosc et Al., 2004) 0.2 / 0.4 g C/m²/day EMED in 1999
Mean: 12.4 mmol.N/m²/d Min/Max: 6.9 /22.3 mmol.N/m²/d
Oxygen 4.28 / 4.91 mL/L in LIW 4.24 / 4.48 mL/L in MDW (Manca et al., 2003)
Mean: 220.3 µmol/kg Min /Max: 202.3 / 235.5 µmol/kg
Bathymetry Max depth : ~ 3500 m / 5121 m Mean: -1002 m Min/Max:-4181/-59 m
In the deep-water of Mediterranean, the water masses are homothermous from 300-500 m to
the bottom. The temperature varies with the longitude: the deep-water in the Western
Mediterranean (WMED) is warmer (15.5 °C) (Table 1) than in the Eastern Mediterranean
(EMED) (13 °C) (Danovaro et al., 2010). However in the shallow water, the column is
stratified in late spring and in summer with a thermocline around 20 to 50 m deep. (Danavaro
et al., 2010). It was also observed that the temperature has increased in water below 200 m
depth to 0.19°C/decade since 1961 (Borghini et al., 2014). Nevertheless, the WMED has
4
become warmer in all water columns whereas the EMED became colder in the shallow water
and mid-water (0-600 m) and warmer in the deep water (below 600 m) between 1970 and
2000 (Rixen et al, 2005; Schroeder et al., 2012). Themelis (1997) and Olivar et al. (2012)
showed that assemblages of mesopelagic and the temperature might be correlated. In
particular, the study of Olivar et al. (2012) stated that the DSL could be localized under the
thermocline.
Mediterranean Sea is the saltiest sea in the world and becomes saltier each year in all layers
of the sea (Borghini et al., 2014; Rixen et al., 2005). The eastern part is saltier than the
western part due to a strong net evaporation (50 to 100 cm/year (Bryden and Kinder, 1991))
The WMED waters are also less dense because the thermohaline circulation begins at the
Strait of Gilbraltar with water masses from Atlantic (Schroeder et Al; Borghini et al., 2014).
Themelis (1997) established mesopelagic fishes distribution was affected by the salinity.
The high oxygen concentration is also one of the main hydrological features of the deep
Mediterranean Sea (Danovaro et al., 2010). The concentration is higher in MAW and LIW
than in MDW where the variation is stable. The concentrations of dissolved oxygen became
higher in the deep layers whereas it has reduced for water above 1000 m depth during those
last decades (Lascaratos et al., 1999). Mesopelagic fish lives during day time in the deficient
oxygen layer (Kinzer et al., 1993). The abundance of mesopelagic fish could be affected and
decline with a reduction of the oxygen concentration (Bianchi and Morri, 2000; Koslow et al.,
2011).
Mesopelagic fish might be related to physical and chemical variables mentioned above.
Besides, it is important to remember that those fish are part of the mesopelagic zone. This
layer could be independently described by other variables than the ones for which the
mesopelagic fishes are sensitive. The temperature, the euphotic depth, the nutrients and the
salinity are the parameters which can differentiate the mesopelagic from the epipelagic and
the bathypelagic layer in Mediterranean Sea (Reygondeau et al., 2015). Only two of them,
temperature and salinity, are going to be part of our models.
Figure 1: Clusters of biogeochemical regions of the mesopelagic layer in
Mediterranean Sea. (Reygondeau et al., 2015)
5
According to Reygondeau et al. (2015), mesopelagic layer could be clustered in eleven
regions based on temperature, salinity, Oxygen, pH, NO3 and PO4 values. Unique
environmental patterns in water masses determine an area – region- having the same
values. E.g., Adriatic Sea (violet zone 9 in Figure 1) has salinity and oxygen values
concentrated around 38.8 psu and 5.75 mL/L respectively. Balearan Sea (green zone 1 in
Figure 1) has also concentrated salinity values around 38.8 psu, but oxygen values fluctuate
between 3.5 and 5.75 mL/L. Those clusters can help in the understanding of the biomass
distribution of mesopelagic fish.
2.1.2. Biological features:
The low nutrient levels of Mediterranean characterize the sea as oligotrophic. However,
those levels contrast with an intermediate level of primary production. The primary production
is generally higher in the WMED than in the EMED. The primary production occurs
respectively in early spring time and in summer time (Bosc et al., 2004). Besides, EMED is
one of the most oligotrophic areas of the world. This pattern is explained by the decrease in
nutrients from west (due to productive waters flows coming from Atlantic Ocean) to east.
(Danovara et al., 2010). Between 1980 and 2011 – period studied in this report, the
interannual changes of primary production did not follow a general trend but partially varied
due to the occurrence of exceptional bloom (Bosc et al., 2004). Primary production and
mesopelagic fish relationship has been demonstrated by several studies : their diet is
composed by an abundant quantity and wide variety of meso and macroplankton (Kozlov,
1995) and Irigoein et al. (2014) recently proved a positive relationship between primary
production and mesopelagic fish.
The distribution of mesopelagic fish is primarily led by the relation prey/predator. As
mentioned in the introduction, mesopelagic fish happens to migrate vertically, mainly for their
diet. It was also reported that some schools of mesopelagic fish can be distributed according
to the pressure of predators (Saunders et al., 2013). A static deep scattering layer where the
majority of the mesopelagic species occur was identified between 200 to 400 m depth by
Peña et al. (2014) in Balearan water. This layer was a permanent pattern seen with acoustic
at 38 kHz. Their DVM was better observed with a lower frequency (18 kHz).
The biomass of mesopelagic fish could be impacted by physically structures such as
seamounts (isolated elevation of 1000 m and more above the seafloor (Menard, 1964)), and
sea-ice coverage. In Mediterranean Sea, 101 seamounts are numbered (Morato et al., 2013)
with the majority located in the Tyrrhenian Sea and along the South East Ionian Sea up to
the South of Crete. Those topological patterns impact negatively the abundance and
community structure of mesopelagic due to a high predation (De Forest and Drazen, 2008;
Pusch et al., 2004).
2.2. Environmental predictors
Climatological data were extracted from a biogeochemical model (Macias et al., 2014) and
from datasets freely available on the network (Table 2). They consisted on an average of
selected parameters (e.g., sea surface temperature, salinity, etc…) over the whole water
column from 1970 to 2011. Then, models used the parameters of a given year, for example
1980, to predict the biomass at this year.
6
As the salinity dataset was not available since 1980, an average between 1987 and 1992
has been done for complementing the missing years (1980-1986). This was done making the
assumptions that salinity has not significantly changed during the 80s.The spatial resolution
used to model was 0.2° to optimize the running of the algorithms although the resolution of
the dataset was 0.0625°.
Table 2: Environmental predictors used in the analyze of the biomass
Environmental variable (unit)
Resolution
Model Available Year
Source
Primary production
(mmol N.m-2.d-1 )
0.0625°
Biogeochemical model
1970-2011
Macias et al.,2014
Oxygen (µmol/kg)
Temperature (°C)
Bathymetry (m) Global terrain
models /
General Bathymetric Chart of the Oceans
(www.gebco.net)
Salinity (PSU) NEMO-OPA
1987-2011
MyOcean follow-on (marine.copernicus.eu)
In the case of the salinity, oxygen and temperature, times series of our environmental
datasets are more and less following the trends described in earlier paragraphs (Figure 2).
Figure 2: Variation of the environmental parameters on the period studied : 1980-2011,
average on the whole Mediterranean Sea.
Temperature and oxygen increase over all the period studied (from 16.4 °C to 16.9°C and
from 214 µmol/kg to 218 µmol/kg respectively). In the case of oxygen, only the deepest
layers are known for having a raise of oxygen and it could explain the increase seen in
38.4
038
.46
38.5
2
z
Sal
inity
214
216
218
z
Oxy
gen
16.4
16.7
z
Tem
pera
ture
1980 1985 1990 1995 2000 2005 2010
9.5
10.5
PP
R
7
Figure 2. The salinity decreases up to 1997 and then increase from 38.40 to 38.54. No
specific trend appears with the primary production. It varies between 9.3 and 11 mmol N/m²/d
on our time-series as what stated Bosc et al. (2004).
Other environmental variables such as pH and dissolved organic Carbon could have been
included in the models as environmental parameters which could influence the mesopelagic
fish biomass. However, their availability was either restrained in time (e.g., the 1980-2011
period was partially covered) or the resolution of the grids was above 0.2°. Therefore, we
could not consider those parameters.
2.3. Biomass information
All biomass data were gathered from scientific literature (from peer-reviewed article to
scientific report) (Annex VI). Conditions were that the literature might provide coordinates
and estimates of the mesopelagic fish biomass. That is why literature mentioning fish caught
by longlines or found in the diet of a predator could not be taken into consideration.
Mesopelagic fish in larval stage was not included in the estimates of biomass.
The species included in the analysis were the ones that accordingly to Fishbase (Froese,R,
Payly D., 2015) were defined as mesopelagic or in some cases, bathypelagic fishes. Indeed,
some fishes defined as bathypelagic could occur in both mesopelagic and bathypelagic layer
(e.g. Arctozenus risso, a bathypelagic fish living in the layer from 0 to 2200 m depth) (see
Annex II). Benthopelagic, reef species and epipelagic species were immediately discarded.
Biomass (in tonnes) was recorded by station (i.e., by longitude and latitude). If only
abundance data were found, then biomass was computed using the length-weight
relationship:
𝑊 = 𝑎. 𝐿𝑏 (1)
with W being the weight (gr), L the total length (cm) of the fish, a and b, coefficients
extracted from Fishbase or from the literature.
A catchability rate was also included in the analyses. In particular, we applied a catchability
of 0.07 for 7 cm or less length, 0.06 for a length around 8 cm and 0.2 for a length even or
higher than 12 cm, as observed by May and Blaber (1989).
Finally, to obtain biomass per surface, we divided the biomass per the trawled area (Strawled)
which can be expressed as:
𝑆𝑡𝑟𝑎𝑤𝑙𝑒𝑑 = ℎ. 𝑣. 𝑡 (2)
where h is the width (m) of the mouth of the trawl, v (m²/s) the speed of the boat and t
(s) the time spent trawling.
8
Figure 3: Distribution of the samples in the Mediterranean Sea and values of the
biomass (t/km²) in each sample.
In total, we gathered 905 samples (including biomass estimates, depth, coordinates and
years of survey) mainly distributed along the north shore of the Mediterranean Sea (Figure
3), particularly in the western part. The biomass varied between 5.36*10-9 t/km² and 5.64
t/km². 13 mesopelagic families were assessed with 3 being well known in the mesopelagic
layer (Gonostomiids, Myctophids and Stomiids) representing 71.8 % of the biomass
sampled.
The biomass extracted and/or estimated in this study represents the total biomass of
mesopelagic fish in the water column.
2.4. Biomass distribution models
In order to visualize the relationship between mesopelagic fish biomass and environmental
data, two kinds of statistical algorithms were fitted to the dataset: random forest and
generalized additive model. In both models, each biomass point was weighted proportionally
to the number of years for which biomass was available at the coordinate point, giving it
more importance. For instance, at a point where biomasses are sampled for 3 years, the
weight is 3. Accordingly to the Pearson’s coefficient of correlation, our predictors were not
correlated between them (i.e., the coefficient was lower than 75 % between a couple of
predictors).
All the statistical analyses and maps were done with R (R Core Team) and QGIS (QGIS
Development Team) respectively.
2.4.1. Random Forest
Traditional Random Forest (hence RF) was chosen over Conditional Inferences Trees (CIT)
and Boosted Regression Trees (BRT) which are all tools used in ecological modeling
(De’ath, 2006; Prasad, 2006). The three of them make a forest of several decision trees grow
before averaging them. That means they fit many models (called decision trees) and
combine them (bagging or boosting) for prediction. Whereas RF randomly selects the
predictor to split the observations, BRT selects variables which maximise the difference
between the two groups. In addition, BRT combines additive regression with boosting
9
techniques which can be recurrent with the other method used in this study (Hastie et al.,
2009). CIT differs from traditional random forest by the concept of a statistical significance
improvement approach while partitioning the training data and the aggregation scheme of the
n trees. (Hothorn et al., 2006b). It means knowing whether the variables selected for the split
will significantly improve the general performance of the model or not. However, except for a
data frame containing categorical variable, ensemble of CIT gives the same results of
traditional RF.
2.4.1.1. General information about Random Forest
Random Forest is a machine bagging learning method which builds a lot of decision trees
after bootstrapping samples from the dataset. The response variable (in our case, log
transformed biomass) is partitioned into small successive binary splits which are based on
single values from different predictors. For example, supposing two variables for the
partitioning: temperature and salinity with a value of 16°C and 37.2 respectively, all samples
having a lower temperature and salinity will be packed together in one side, and samples
having higher values, in another side. From the first new pack, two news variables will be
used (for example, oxygen and salinity) to split it once again while the second pack will
process identically but independently with other variables.
At each node of the trees (i.e., the packed data formed between two splits), the split kept is
the best one among all the values of the selected variables used for splitting (e.g., splitting
with a temperature of 16°C instead of 17°C). Then, RF algorithm does an averaging of all the
decision trees which have grown independently with the training dataset – the dataset used
to fit the model (Figure 3).
Figure 4: Process of random Forest from n trees to get the model.
RF performs automatically a cross-validation with one third of the dataset kept out of the
training dataset. This one third, called the Out-of-Bag (OOB) data will be used to define the
model accuracy to predict in computing the mean of square error (MSE) measure: MSEOOB.
The MSEOOB will allow to choose the number of tree needed in the forest. It will generally
decrease when the number of tree is increasing.
Unlike linear regressions, the normality of the data structure is not required by RF. Random
Forest can model and predict non-linear response variables with a little number of samples
and large number of predictors even if interactions and correlations among variables exist.
The predictors can be either a mix of continuous and categorical variables (Breiman, 2001)
or one type of variables. However, RF does not tolerate NA values. Data needs to be fixed
Dataset
BootStrapped Sample 1
Tree 1
OOB sample 1 ...
Tree k
... Bootstrapped
sample n
Tree n
OOB sample n
MSE MSE MSE
AVERAGE of the n TREES
10
either by performing a local average, by using a function (‘NAroughfix’) included in the
Package randomForest R, or by removing those NA values.
For regression RF, the model can be understood by the following equation (Hastie et al.,
2009):
𝐹(𝑥) =1
𝐽. ∑ 𝐶𝑗 𝑓𝑢𝑙𝑙
𝐽
𝑗=1
+ ∑(1
𝐽. ∑ 𝑐𝑜𝑛𝑡𝑟𝑖𝑏𝑗(𝑥, 𝑘)
𝐽
𝑗=1
)
𝐾
𝑘=1
(3)
J : number of trees in the forest K : number of nodes Cjfull : is the value of the response at the root of the nodes (average value of the response in the training dataset), Contribj(x,k) is the contribution of the kth feature in the feature vector x (i.e., the difference between the averaged response at the node k and at the node k-1).
The theoretical prediction function of Random Forest, in the case of regression, is the
average of the bias terms plus the average contribution of each feature. This shape is quite
similar to the multiple linear regressions except that the independent variable coefficients
used for the split cannot be provided.
The performance of the model (percent variance explained) is useful to compare this model
with the generalized additive model (GAM). The computing is based on MSE between
observation and OOB predictions.
MSEoob = n−1. ∑(yi
J
j=1
− yiOOB)² (4)
and then :
R2 = 1 −MSEOOB
θ̂y2
(5)
n, the number of samples, yiOOB the average of the Out of Bag predictions and θ̂ the
standard deviation of the estimates (Liaw and Wiener, 2002).
Although the number of studies using RF is increasing in research, a bias has been noticed
with an overestimation of small values and an underestimation of large values. In order to
correct this bias, a methodology from literature has been followed to generate the bias
correction (Xu, 2013; Zhang and Lu (2012))
The steps described by Zhang and Lu are:
1- Random Forest is applied on the response (𝒀𝒐𝒃𝒔) with the predictors (Xi, (i=1,…,n)).
2- The bias �̂� is obtained : �̂� = �̂� − 𝒀𝒐𝒃𝒔
3- Random Forest is applied on the bias �̂� with the predictors (Xi, (i=1,…,n))
4- A corrected estimation of the biomass is get for each sample of the training data:
�̂�𝒃𝒄 = �̂� − �̂�𝑹𝑭
11
The linear regressions can provide the predictor coefficients of the regression and their
importance in the variance explained by the model. But RF only provides the variable
importance: the predictor which impacts on the model performance if it is discarded from the
model. This impossibility to interpret how the variable might impact the response is why RF is
considered as a black box.
For regression random forest, two kinds of variable importance are provided: one is the
computing of the increasing in MSE while a permutation of a predictor occurs, e.g. the
difference between MSE from the model with all variables and the same model without one
of the variables. The other is a measure of the increasing node purity by residual sum of
squares (RSS) which looks at how the permutation of a predictor can improve the RSS at
each node of the tree (Hastie et al., 2009). The variable importance shows the contribution of
the predictors to the model accuracy but it needs to be careful handed: if a high correlated
variable is present among the predictors, the importance of this variable is affected and is
relatively big (Gregorutti et al., 2013). In that case, the predictor must be discarded.
2.4.1.2. Parametrisation of Random Forest
Random Forest was run using the package randomForest (Cutler and Wiener, 2012). Among
all the packages running random Forest (party, quantregForest), this package was the one
providing understandable outputs and fast results. This package was also well described and
its advantages and drawbacks enough known. After a short run of CIT with ‘cforest’ from
party package (Hothorn et al., 2015, version 1.0-22), it was noticed the results were similar.
Random Forest needs two entries for running: a number of variables used for the split and a
number of trees.
The number of predictors used for each split was fixed to 1 predictor (e.g., temperature,
salinity) thanks to the function ‘TuneRF’ from the package randomForest. No pruning – fact
to limit the number of nodes in a tree, was applied. The response variable was then
bootstrap resampled to generate large number of un-pruned decision trees. This number of
trees has been settled to 400 trees. With fewer trees, the performance of the model was
unstable and the importance of variables was hesitant about the order of the variables. With
more trees, a risk of overfitting the model could rise up. 400 trees allow us to have a pseudo-
R² stabilized (Breiman, 2001).
Once the parameters were defined, the running was done with seeds set to 1000. The
expected outputs were the importance of variable, the mean square error and the pseudo R².
Partial Dependent plots which are graphs plotting the predictor versus the response when
the other predictors are held at their mean values can also be obtained (Elith et al., 2008).
2.4.2. General non parametric regression model
2.4.2.1. Optimal transformation with ACE
The Alternating Conditionnal Expectation (ACE) is a tool used to seek transformations of the
independent variable which maximize the correlation between the response (log transformed
biomass) and the predictors (temperature, salinity,…) (Brieman and Friedman, 1985). It
allows nonmonotonic transformations of all variables. It is through the shape of the plots of
the transformed variable versus the observed values of the variables that we can obtain the
12
type of transformations required for regression analysis. ACE was applied thanks to the
package Acepack (Spector et al., 2014). The output are representing in Figure 5.
Figure 5: ACE transformation of independent variables included in the regression
The relationship between environmental parameters could be expected as linear for the
oxygen and the primary production. Some discontinuities in the plots of Oxygen (~220
µmol/kg) and PPR (12.5 mmol N/m²/d) could be associated to weird values of the predictors.
Although the shape of the temperature against the biomass seems to be segmented, a
logarithmic transformation could be applied on this parameter and also on the bathymetry
variable. For the salinity parameters, the linearity cannot be assumed without segmenting the
variable or having a non-linearity relationship. The distribution of this variable shows two
modes (Annex III). This result led us to use a generalized additive model (GAM).
2.4.2.2. Generalized additive model regression
GAM is an extension of the linear model that allows several transformations on the
independent variables. Like RF, it can deal with non-linear data.
205 215 225 235
-1.0
-0.5
0.0
0.5
1.0
Oxygen
-4000 -2000 0
-0.6
-0.4
-0.2
0.0
Bathymetry
10 15 20
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
PPR
37.5 38.0 38.5 39.0
-3-2
-10
Salinity
15 16 17 18 19 20
-0.5
0.0
0.5
1.0
1.5
Temperature
ACE
Bio
ma
ss
13
The results of the optimal transformation with ACE and the distribution of the observation
(Annex III) lead to use a semi parametric regression model to fit the data. Indeed, except the
salinity predictor, all the environmental parameters could have a linear relation without or
with a logarithmic transformation.
Semi parametric GAM is derived from the original equation of Hastie and Tibshirani (1986):
𝑔(𝑦) = 𝜇 + 𝛽1. 𝑥1 + ⋯ + 𝛽𝑘. 𝑥𝑘 + 𝑚𝑘+1(𝑥𝑘+1) + ⋯ + 𝑚𝑛(𝑥𝑛) + 𝜀 (6)
Where , g() is the monotone link function, the partial-regression functions mk are fit
using smoothing splines for non-parametric variables, µ the intercept, βk, the coefficient
of parametric variables and 𝜀 the normally distributed random error
GAM is a model which is fit by penalized likelihood maximization. Smoothing parameters,
when unknown, are solved using the Generalized Cross Validation (GCV) criterion (Wood,
2015):
𝐺𝐶𝑉 =𝑛. 𝐷
(𝑛 − 𝐷𝑜𝐹)2 (7)
Where n is the number of samples, D, the deviance, and DoF, the effective Degrees of
Freedom.
GAM has been implemented by using the ‘mgcv’ package (version 1.8-7 from Wood, 2015).
The selection of a final model followed several criteria: (i) the estimated degrees of freedom
must not be close to 1, (ii) the confidence interval should not be close to 0 along the smooth
function plot, (iii) GCV is decreasing if one of the covariates is dropped. Then, the results of
the selected model should not (i) give unrealistically biomass and (ii) errors with the
estimation of uncertainty of biomass (Wood, 2001).
With an identical link function, the Gaussian model structure was:
𝐿𝑜𝑔(𝐵𝑖𝑜𝑚𝑎𝑠𝑠) = 𝑃𝑃𝑅 + 𝑂𝑥𝑦𝑔𝑒𝑛 + log(𝐵𝑎𝑡ℎ𝑦𝑚𝑒𝑡𝑟𝑦) + log(𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒) + 𝑠(𝑆𝑎𝑙𝑖𝑛𝑖𝑡𝑦) +
log(𝑏𝑎𝑡ℎ𝑦𝑚𝑒𝑡𝑟𝑦) : 𝑂𝑥𝑦𝑔𝑒𝑛 (8)
Where s(), the smooth function, is a thin plate regression spline.
The basis dimension parameter k was set to 5, and thereby, reducing the highest degrees of
freedom to be 4. This choice is coherent with the needed conditions mentioned above. A low
value for this parameter has also the advantages to avoid overfitting and be more adapted to
an ecological issue. The parameter gamma, a correction ad hoc which inflates the degree of
freedom in the GCV, was set at 1.4 for avoiding the overfitting of the data as proved with Kim
and Gu (2004).
Interaction terms between the bathymetry and the other independent variables were
regarded as potential effect on the biomass. However, only one interaction was included in
the model considering that the conditions were not filled with the other terms.
Outliers underlined by a distance of Cook bigger than a threshold of 0.05 was discarded from
the data set.
14
Finally, the deviance explained by the predictors was computed by step-wise approach
where each term was dropped from the final model. The residual deviance of the final model
was subtracted by the residual deviance of the model without one variable.
The comparison between GAM and RF was mainly done with the performance of the models
and their prediction. However, to complete the study of the performance a 10-folds cross-
validation was done 100 times. The root-mean-square-error (RMSE) was then compared.
2.5. Prediction of biomass
Once models calibrated, they were used to predict the total biomass for Mediterranean Sea
in a generated grid of 0.2° x 0.2° resolution. First, the biomass was predicted for each year
since 1980 to 2011. The total biomass per year was got by integration with the following
equation:
Σcell(Areacell x BiomassCell) (9)
Considering the resolution, the latitude and longitude of the Mediterranean Sea, the area of
all cells was: 22.24 x dcell km².
The distance between 0.2° latitudes did not change when longitudes change in
Mediterranean Sea whereas the distance between 0.2° longitudes changed when latitudes
change. In order to consider this variation in distance, di was computed by the following
method:
𝑑𝑐𝑒𝑙𝑙 = 𝑅. 𝑎𝑟𝑐𝑜𝑠𝑖𝑛𝑒(sin(𝑟𝑎𝑑(𝐿𝑎𝑡𝑐𝑒𝑙𝑙))2
+ cos(𝑟𝑎𝑑(𝐿𝑎𝑡𝑐𝑒𝑙𝑙))2 × cos(𝑟𝑎𝑑(0.2))) (10)
For one cell, R is the radius of the earth (6371 km), Latcell the latitude and ‘rad’ explains
the transformation from degree to radian unit.
In order to figure out where the biomass could have significantly changed, the variance by
cells across all the years was computed (Equation 11) and projected. All projections were
done in the World Geodetic System (WGS) 84 reference.
∑ (𝑥𝑖 − 𝑥)2𝑖
𝑛 (11)
where xi is the biomass (t/km²) at the year i, 𝑥 the average of the biomass for a cell and
n the number of year.
15
3. RESULTS
3.1. Comparison of model performance
Random Forest performs with a pseudo R² = 76.5 %. After adding the bias correction, the
model overestimates the large values and underestimates the low values (Figure 5.a)). The
difference between the model with the bias correction and without the bias does not change
efficiently the results. GAM performance is lower than RF with an adjusted R² of 47.2 % and
GCV of 9.66. According to Figure 5.b), GAM tends to overestimate the low values and
underestimates the large values. Most of the biomass is situated in a range between -10 and
0 log t/km² with Random Forest and GAM (though this last one is less centered on the
mean).
The analysis of the residuals indicates the GAM model to be acceptable. The
homoscedasticity of residuals is respected although the normal distribution of the residuals is
unbalanced for smaller residual values (e.g., residuals from - 5 to -10 log t/km²) which may
be related to the overfitting of little values. Residuals could also be independent and
generally follow a Normal distribution except for a range between -5 and -10 log t/km² (Annex
IV).
Figure 6: Scatter plots of observed versus predicted mesopelagic fish biomass from a)
Random Forest model and b) GAM. The blue dashed line is the 1:1 line, the black line
is the fit. The black dashed line in a) is the fit without the bias correction.
Random Forest considers all the predictors non-linear. On Figure 7, no linear relation ship
appears except for segments of primary production (from 10 to 17 mmol N/m²/d) or oxygen
(from 205 to 217.5 µmol/kg). The flatness observed for values upper than 17 mmol N/m²/d in
the case of primary production and lower than 37.8 for salinity is only due to lack of data.
b) a)
16
Those results are similar to the ones obtained from the ACE algorithm. The salinity almost
follows the same shape than the salinity from Figure 5. This is the case for temperature as
well where there is a distinct change around 16°C. The similarity with oxygen and bathymetry
is not clear. Nevertheless, primary production trends are totally different. While RF seems to
have a primary production which mainly rises up, the ACE plot of this parameter show a
decrease of the biomass when the environmental predictor increases.
Figure 7: Partial Dependence Plot from Random Forest model. The y-axis is the
absolute value of the prediction when other variable are fixed.
A summary of variable importance is given in Figure 8. It can be seen than salinity is the
most influent parameter in the Random Forest model. This predictor increases the MSE of 40
% when salinity is permuted from the training. Oxygen, with 38.3 %, is the second most
influent parameter. Temperature and bathymetry are considered as having an even
influence. Finally, the primary production is the variable having the less influence on the
model with 30.6 %. The absence of a high variable importance confirms the non-correlation
between the predictors.
Besides, the variable importance order with the second method, the increasing in node
purity, is the same.
17
Figure 8: Predictor variable importance in Random Forest approaches with their
confident interval.
The deviance explained by each predictor (Table 3) also implies that the salinity explained a
majority of the model deviance. Indeed, the difference in residual deviance when salinity is
excluded from GAM is 8864. This important value is 100 times higher than the other
differences. With GAM, the interaction is the second predictor explaining the model whereas
it is the oxygen with RF. The primary production is once again in the variables which
contribute the less into the model.
Table 3: Parameters contribution to GAM model’s deviance.
Parameter ΔResidual Deviance
Salinity 8864.0
Interaction 93.64
Log(Bathymetry) 88.99
Oxy 53.12
Log(Temp) 52.28
PPR 39.54
Figure 9 shows how the logarithmic transformed biomass changes relatively to the salinity
when the other variables of the model are held constant. The shape of the curve is similar to
the one get with the ACE transformation (see Figure 5) and have a trend which looks like
with the one get from the dependence plot of Random Forest (Figure 7).
18
Figure 9: Form of the smooth functions for the selected covariate. Black solid line is
the smooth function estimate with grey intervals representing the 95% confidence
intervals.
Finally, GAM returns statistically significant coefficients which correspond to the shape of the
plots obtained from ACE algorithm. For instance, the primary production had a negative
linear relationship with ACE, and its coefficient through GAM is also negative. The only
exception is for the negative coefficient of the oxygen and this might be due to the interaction
with the bathymetry.
Table 4: GAM significance levels of models terms.
Parametric coefficient
Estimate Standard Error
t-value Pr(>|t|)
(Intercept) 70.68376 22.98611 3.075 0.00217
Oxygen -0.22877 0.09551 -2.395 0.01681
PPR -0.04155 0.01942 -2.140 0.03262
Log(Bathymetry) -9.40316 3.05023 -3.083 0.00211
Log(Temperature) -9.38056 4.08206 -2.298 0.02179
Log(Bathymetry)/Oxygen 0.04360 0.01376 3.169 0.00158
Approximate significance of smooth terms:
Estimated df Ref. df F p-value
S(Salinity) 3.972 4 225.9 <2e-16
37.5 38.0 38.5 39.0
-2-1
01
23
4
Salinity
s(S
alin
ity)
19
The best performance judged on RMSE (Figure 10) is described by the Random Forest
model with the lowest value of RMSE. While the average is at 1.79 for RF, the GAM’s
average is at 2.80 with cross-validation.
Figure 10: Boxplot of the RMSE got from a 10 folds cross-validation repeated 100
times.
3.2. Comparison of the estimation of the biomass in Mediterranean Sea
3.2.1. Biomass in 1980, an example
Between the two models, the distribution of the mesopelagic biomass differs (Figure 11).
Both models seem to predict less biomass in the Ionian basin and a biomass in the range of
more than 0.1 t/km² in the Alboran Sea. Along the Lebanese and the Egyptian coast, the
presence of mesopelagic is estimated by the two models but a different level of biomass. The
biomass of mesopelagic fish with Random Forest is concentrated in the Ligurian Sea and
between the Baelaric Island and Sardinia/Corsica.
Random Forest strangely predicts a biomass between 0.01 and 0.05 t/km² in the Adriatic Sea
(Figure 11.a)) where the bathymetry is less than 200 m depth. The Gulf of Gabes seems also
being a place with relatively elevated biomass whereas it still along the continental shelf.
Regarding the GAM (Figure 11.b)), the biomass of mesopelagic between 0.01 and 0.05 t/km²
is noticeable along the Algerian Coast and in the Aegean Sea up to the Crete. High biomass
occurs in the Gulf of Lions and along the Spanish coast in the Western Balearic Sea.
However, the uncertainty measured with the coefficient of variation underlines the fact those
predictions are not reliable. In those areas, the CV (Annex V) is between 30% and 40 % of
variations. Those same values are also seen in the northern part of the Adriatic Sea and in
the centre of Aegean Sea. The CV is under 10 % for the other parts of Mediterranean Sea.
20
Figure 11: Distribution of the predicted biomass in 1980 with a) RF and b) GAM.
3.2.2. Temporal biomass estimates
The temporal estimates of biomass for both models have some variability (Figure 12).
Using RF, mesopelagic biomass seems to gradually increase from 1980 to 2000 and a
strong decrease from 2004 to 2011. The biomass range is approximatively between 25,000 t
and 35,000 t in the 31 estimated years.
Using GAM, no clear trend can be observed. However, biomass seemed to have increased
between 1988 (19,500 t) and 1993 (27,900 t) then decreased again until 1998. In GAM,
biomass varies between 19,500 t and 27,900 t in the 31 estimated years (1980-2011) with an
uncertainty of 100 t in average.
If the range of biomass is quite similar between models, the estimate from RF is bigger than
the GAM estimates. RF estimates are, on average, 35% more important than GAM. It could
also be noticed than the variation of biomass is not synchronic. Also, we cannot notice an
important variation of the biomass between 1988 and 1998 in RF. These differences show
the models to act differently with different prediction.
21
Figure 12: Biomass of mesopelagic fish from 1980 to 2011 estimated by RF
(continuous line) and GAM (dashed line).
The change of the biomass through the 31 estimated years is different from both the models.
Except to the Gulf of Gabes where there is a variance of 0.8 along the continental slope, all
the important variances are pooled separately. With Random Forest (Figure 13.a)), variance
is the highest (> 1.8.10-3 (t/km²)²) where the biomass was already relatively high (Figure
11.a)) at the North Aegean Sea, along the Egyptian coast and in the Ligurian Sea.
22
Figure 13: Variance of the mesopelagic biomass for 31 years: 1980-2011 extract from
a) RF and b) GAM
Regarding GAM estimates (Figure 13.b)), the pattern saw in Figure 11 is also noticeable.
Alboran Sea and South-West of Balearic Sea have a higher variance (upper than 1*10-3
(t/km²)²). The biomass in Aegean Sea around the Crete has been stable during the period of
the study. There is just a slight change of variance (1 to 1.6 *10-3 (t/km²)²) in the center of this
sea. Variation of the biomass is also visible along the continent slope on the boarder of the
200 m depth. Then it could be supposed that the increasing of biomass observed between
1988 and 1998 might be due to an important change in the areas where there is high
variances.
23
4. DISCUSSION
4.1. Methodological considerations: validity of the data
4.1.1. Reliability of the environmental data
Since environmental data were extracted from biogeochemical models, uncertainties exist
around our results and their interpretations. The primary production, for example, is
surprisingly low in some parts of the Mediterranean Sea like along the Egyptian Coast, on
the Tunisian continental shelf and in the shallow Adriatic water. Some samples were taken in
those ranges of low PPR values (see Figure 3 and Annex I: East of Aegean Sea). Those
samples could have influenced the models to predict a biomass value relatively high in
places with low PPR and therefore, a low bathymetry. Besides, our training dataset only
explained 6 % of Mediterranean Sea. That low rate of coverage could imply an extrapolation
of the biomass.
Using time series of survey data for all the variables (instead of modelled climatological data)
would probably result in a better model performance; however this would be a challenging
task for modelling biomass at larger scale (global estimates).
Environmental data were average values of the different layers of water column. As some
environmental parameter datasets were established on different ranges of depth like the
PPR, which is occurring in shallow waters whereas salinity was provided on deeper layer, the
integration was useful to homogenize the datasets in one unique layer. The environmental
data sets were therefore integrated over the entire water column instead of including only
those from the mesopelagic layer. This computation modifies strongly the reality of the
mesopelagic distribution. Indeed as seen in table 1, the variables associated with the sample
of biomass have a different value from the layer where the deep-sea fish are usually found.
For instance, the temperature of the sample has 16.1° C in average while the temperature of
the LIW is ranged from 13.0 to 15.5°C (Zavatarelli and Mellor, 1994).
4.1.2. Miscalculation of the observed biomass.
A possible source of bias comes from scientific literature and all the parameters that were
extracted from it. Mesopelagic biomass was obtained from publications (Annex VI) on
Mediterranean Sea studies of marine ecology (e.g., the size relationship, the assemblages of
species or their diet). The scope of such articles was generally not related to estimate
mesopelagic fish biomass. Also, mesopelagic biomass quoted represented only a little
sample of the collected biomass as they only selected the most significant species of trawls.
The most common family, Myctophidae, was often mentioned as fish present in the trawl but
with no data or information provided.
The second source of bias might be related to the coefficient used in the weight-length
relationship. Although the most appropriate coefficients were used to compute fish biomass
when only abundance data was included, in some cases, lack of information (e.g.,sizes) led
us to use default values as the one provided Fishbase. For example, Stomias boa boa’s,
whose size was not available in the scientific article, was given a standard length of 32.2 cm,
as provided by Gibbs (1990) in Fishbase. By applying the length-weight relationship to it and
a catchability of 0.2, the biomass of one Stomias boa boa is 313.5 g. This weight
corresponds to the maximum weight of the fish but do not represent the real weight of the
24
catch as we used a general size. In one way or another, the biomass of mesopelagic species
could have been either overestimated or underestimated.
4.1.3. Catchability cause.
In this study three constant catchabilities were used to estimates the biomass present in the
water column. They were accurate for a given size of a given species with a given gear (May
and Blaber, 1989). For instance, Diaphus danae’s common size is around 12 cm (Fishbase)
and has a catchability of 0.2, meaning that only 20% of the total biomass in the water column
was caught. In May and Blaber’s study, it was caught with an Engel trawl and a Simrad FB
trawl eye. It is a strong assumption not to change the catchability when the species, the gear,
the size and the behaviour of the fish and the type of net changes because a catchability
depends on biological and technical factors (Arreguín-Sánchez, 1996). For instance,
Maurolicus muelleri has a catchability of 0.06 with an Engel trawl (May and Blaber, 1989)
and a catchability of 0.15 with an Akra trawl (Heino et al., 2010).
An alternative way to correct those catchabilities can be done using a ratio. The basic
assumption is using two independent methods for estimating the biomass on an area: for
example from trawl surveys and acoustic surveys. The ratio between these two estimates
provides the range of difference between the two methods
𝐵𝑎𝑐𝑐
𝐵𝑡𝑟𝑎𝑤𝑙= 𝑎. 𝑞 (12)
Being a, the coefficient which can modify the catchability used in the data set.
However, this approach cannot be applied since in Mediterranean Sea, no publications about
acoustic survey related to trawl survey on mesopelagic fish were found. In order to get the
coefficient a, we should look at different places investigated by those methods such as
Chatham rise in Australia or along the north American coast Oregon or California.
Additionally to a change of catchability, the avoidance of the gear was not considered and it
is an important origin of underestimation of the biomass (Kaarvelt et al., 2012).
4.2. Model performance
It has been observed than RF performs better than any other models used for predicting a
dependent variable: it predicts biomass with the lowest error (Knudby et al., 2009). In this
study, the RF performance is also better than the one from GAM: the variance explained by
RF is more important and the RMSE smaller. The coefficient issued from GAM have also a
large standard error (Table 4) which might aware us about the important uncertainty around
the parameters. All the statistical analyses lead to prefer the use of RF instead of GAM.
However, the problem resides in statistical interpretation of the RF model where we only
have the partial dependent plot and the variable importance to understand the effect of an
environmental variable. The trouble is also about the certainty of the predictions and
estimates. Whereas it is easy to get confident and prediction intervals with a traditional
regression, the principle is more complicated in the case of RF.
Confident and prediction intervals can be computed with the distribution of the response. In
the case of RF, the function only keep in memory the n trees prediction used during the
running (e.g., in our case, 400 trees were grown, we had 400 biomass predicted for each
25
data point). The confident or/and the prediction intervals will be built from the distribution of
400 predicted biomasses. Besides, those 400 biomasses will be used to construct the forest
prediction. There is no possibility to get the uncertainty around the mean for each tree with
the package randomForest. The use of ‘quantregForest’ package (Meinshausen and
Schiesser, 2005, version 1.0) might be helpful for getting the percentile statistics of each tree
but the standard deviation or the coefficient of variation was not provided.
In some way, we could get the coefficient of variation based on the 400 trees built for each
point of the Mediterranean Sea, however, this resulted in unrealistic values (Annex V). Low
values of CV with random Forest (0-20 %) are found in the Levantine basin and in the Ionian
Sea. Values can be more than 100 % along the continent shelf and where the bathymetry is
relatively low. However the CV computed from trees cannot be trusted (Wager et al., 2014).
A method proposed to obtain those uncertainty values is the Jackknife approach.
4.3. Estimates of biomass.
Considering biomass estimates in 1980 and then the variance during the 31 years, GAM
seems to be the more accurate with the distribution of mesopelagic biomass: i.e., there is not
an important biomass predicted along the coasts or in shallow water of Adriatic Sea.
However, regarding the fact the boundary between mesopelagic and epipelagic might be at
different depths (Reygondeau et al., 2015) due to more turbulent water and an euphotic zone
shallower, the high biomass estimates from RF in those shallow water could be realistic.
Relatively to the Reygondeau et al.’s report (2015), high mesopelagic biomass distributed by
RF (Figure 11.a)) are included in biogeochemical regions (Figure 1) where the temperature is
low (~13.5 °C) a salinity near to 38.5 psu in average and the oxygen range is limited by 4 and
5.5 mL/L.
Figure 14: Rate of the climate change pressure on Mediterranean Sea according to
Reygondeau et al. (2015)
The distribution of high biomass from GAM (Figure 11.b.) is in the same biogeochemical
regions plus in the Adriatic Sea where salinity is higher (38.5-39 psu) and the oxygen limited
to a smaller range: 5-5.5 mL/L. Then we can also establish that the distribution of biomass is
more important in WMED than in EMED where the climate change pressure are more
important (Figure 14, Reygondeau et al., 2015) and the environmental parameters less
extreme.
26
Gjøsaeter and Kawaguchi (1980) have also estimated a biomass of mesopelagic fish more
important in WMED than in EMED with 2.1*106 t and 0.35*106 t respectively.
Comparing to Gjøsaeter and Kawaguchi, the biomass estimated by these models is far away
smaller (Table 5), with a factor of 100. These results indicate an error in the model or in the
computation of the biomass which can be related to the catchability and the lack of data in
some parts of the Mediterranean Sea. In order to call in question models, a basic
computation was done. For one observed biomass on a specific area, we extrapolated this
biomass to the whole Mediterranean Sea area. For example, Lampanyctus crocodilus
biomass was 0.002 g/m² on a swept area of 250,000 m². When the value is extrapolated to
2.5*106 km², the biomass was 24,000 t. We could see that biomass was in the same ranges
than the ones from models (e.g., 25,000 to 35 000 t with RF; 19,000 to 27,000 t with GAM).
The model cannot be the source of the low biomass but could be our biomass data.
Table 5: Comparison between the results of the study and the result from Gjøsaeter
and Kawaguchi:
Area Gjøsaeter and Kawaguchi (1980)
RF (1980) GAM (1980)
Mediterranean area between -5° and 10° longitude
3 t/km² 0.022 t/km² 0.016 t/km²
Total Mediterranean Sea
2.45*106 t 0.29*105 t 0.21*105 t
Regarding the results from 1980 (Figure 11), some patterns of unexpected areas with high
biomasses might be realistic. Indeed, the literature mentions the presence of larvae in
several high value of biomass predicted by models such as at the North of Cyprus (Çoker
and Cihangir, 2015), in Adriatic Sea (Dulčić and Ahnelt, 2007) along the Egyptian Coast
(Fowler, 1986) and in the golf of Gabès (Bradai and El Ouaer, 2012; Zarrad et al., 2013).
On the RF results (Figure 11.a), an important feature is quite interesting: important values of
biomass estimates (0.01-0.1 t/km²) are concentrated in the Ligurian Sea, in the North of the
WMED. Besides, in the publication of D’Ortenzio and Ribera d’Alcala (2009), a pattern in the
distribution of the chlorophyll a (Figure 15) is underlined in that same area. The pattern is a
cluster which represents a place where a bloom of primary production annually occurs.
Having important biomass estimates where a bloom is happening might show that
mesopelagic fish biomass predicted with RF is related to the primary production. That fact
could question the variable importance (Figure 8) and the weak difference in increasing of
MSE between salinity and PPR. Indeed, it may imply the importance has not a strong role in
the prediction of the biomass. Due to the well-known correlation between primary production
and mesopelagic fish established by scientific research, this spatial match between biomass
estimates and primary production bloom is a positive point in the validation of the model.
In Adriatic Sea, there is a coastal distribution of chlorophyll a (Figure 15 and Annex I) which
might partially explained the important estimates in this area by RF. However, a lack of data
could also drive the model to estimates the same biomass in that area that another having
similar environmental parameters.
27
Figure 15: Spatial distribution of the 7 clusters of the chlorophyll dynamics in
Mediterranean Sea (Sources: D’Ortenzio and Ribera d’Alcalá, 2009).
Regarding the Alboran Sea, this is a place of mixing water between Atlantic and
Mediterranean. The important values of biomass estimating there by both models might be
related to those waters. An analysis on the biomass distributing in the neighboring Atlantic
waters could help determining the origin of these patterns.
Making an interpretation of a possible increase or decrease of the biomass is dangerous.
The 1980-2011 period obviously cannot describe the before 1980 biomass. Compared to
several years ago, the biomass represented can also be at a plate, either because the
biomass was more important before or because it was lower in the beginning of the twentieth
century. We cannot link the biomass evolution to a human pressure either because of the
core of this study. Therefore, the variability observed during the 31 estimated years is only
partially explained by our variables. The pattern implemented by GAM in Figure 12 is a
wonder. It might be due to a higher salinity that occurred especially in those years (Figure
16).
There was, between 1988 and 1998, a climate shift in the area of Aegean Sea (Theocharis et
al., 1999, Zervakis and Georgopoulos, 2000). The water from Adriatic Sea is usually the
source of water in EMED. But an outflow of dense water from the Aegean Sea sank into the
deep layer of the EMED in 1987 and continues to sink the following years. When the GAM
estimates bigger biomass in 1993 in the South of Crete, it could be due to the switch of the
source water and therefore, the higher salinity and lower temperature which mixed into the
deep layer. Danovaro et al. (2004) also illustrated a general increase and decrease of the
biodiversity indexes and abundance between these years. This patterns show how the GAM
is sensible towards salinity. In contrary, the RF was not influenced by this phenomenon
whereas salinity was one of its most important variables.
28
Figure 16: Cartography of the salinity (on the left side) and the biomass estimated by
GAM (on the right side) for the a) 1988, b) 1993, c) 1998 years.
5. CONCLUSIONS AND PERSPECTIVES
Mesopelagic fish biomass is a response variable which requires a non-linear model. Applying
a generalized additive model or a decision tree-like model appears to answer this need.
Random Forest can deal with several combined relationships between the response and the
29
independent variables and consists in a friendly model which can easily well perform.
However, it can overfit the data and is a black box with which the interpretation of the
variables influence is somehow impossible to define. The generalized additive model
considers the non-linearity between the variables and gives expected relationships
established by an alternative conditional transformation. It is easy to increase the degree of
freedom required in the GAM but the model will overfit the data more than predict the
biomass. Working simultaneously with these two tools allowed us to check for mistakes
either in the data or un the models and for model outputs ecologically meaningless. Finally,
the two models result in simple models which use the primary production, the salinity, the
temperature, the oxygen and the bathymetry. With GAM, an interaction was added. Both of
them appear to be sensitive to salinity, unique non-linear variable in GAM. The statistical
analyses reveal Random Forest to perform better although GAM’ statistical analyses are still
correct. The main issue is the difficulty to get uncertainty with Random Forest.
The two models have shown totally different results in relation to the distribution and the
variation of the biomass in Mediterranean Sea. Random Forest estimates a biomass 35%
higher than the estimates of the GAM. Both models estimate a higher biomass in the
Western Mediterranean Sea which could be expected according to the literatures. However,
the two models did not show a trend in the variation of biomass during those last years.
Regarding the climate change which modifies the waters where mesopelagic fish live, it was
expected to see either a decrease or an increase of the estimates. Only GAM estimates an
important change relative to changes in salinity. With those estimates, the reliability of
Random Forest is questionable, it estimates biomass in Mediterranean’s area where the
bathymetry is upper than 200 m depth. This fact was not expected regarding the distribution
of mesopelagic fish in the water column and favours the selection of GAM.
Also, a biomass estimated by the both model is 100 times smaller than the biomass
estimated by previous studies and this could be due to several miscomputation of the
biomass as the catchability used or the poor information given by the literatures.
All the results obtain here could not enhance the use of Random Forest or GAM in
particularly. Keeping the two methods and applying them to different case studies or to a
global ocean could finally help assessing which model is the most accurate to estimate
mesopelagic fish biomass. As Random Forest is working by making bags, the fact to enlarge
the training data could let it perform better instead to restrain it at a semi-enclosed area
where the variability in the environmental predictors is quite important.
Another approach in order to evaluate the biomass of mesopelagic fish is to go through a
presence/absence model using the same methods. For instead, a GAM method using a
different family (Binomial) and ensemble of decision tree like Boosted Regression tree or
Random Forest can predict the occurrence of the mesopelagic fishes in Mediterranean Sea,
using the same environmental parameters (e.g., salinity, temperature, etc..). The idea will be
to predict the occurrence species by species and then, applying the weight-length
relationship with an average length of the species. The whole species biomass could lead to
a global estimate of the mesopelagic fish biomass in Mediterranean Sea. This alternative
could be done with the biomod2 or dismod r package which applied several algorithms
specified by the user for obtaining the occurrence.
30
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Annex I: Maps of the environmental predictors
Annex I: Maps of the environmental predictors
ix
Annex II : Mesopelagic fish families and species of the Mediterranean Sea
Family Species Family Biomass (t/km²)
Criteria of selection to determine if the bathypelagic species will be integrated to the study
Centrolophidae Centrolophus niger Centrolophus nigerrimus Schedophilus medusophagus
0.53
Depth range
Evermannellidae Evermannella balbo 0.005 Mesopelagic distribution
Gonosotomatidae Cyclothone braueri C. pygmaea Gonostoma denudatum Maurilicus muelleri
30.84 Depth range
Macrouridae Nezumia sclerorhynchus 24.11 Depth range *
Microstomatidae Nansenia oblita 3.6x10-6 Depth range
Myctophidae Benthosema glaciale Ceratoscopelus maderensis Diaphus holti D. metopoclampus D. rafinesquii Electrona risso Gonichthys coccoi Hygophum benoiti H. hygomii Lampanyctus crocodilus L.pusillus Lobianchia dofleini L. gemellarii Myctophum punctatum Notoscopelus elongates N. bolini N. kroyeri Symbolophorus veranyi
54.11
Mesopelagic distribution
Nemichthyidae Nemichthys scolopaceus 0.022 Mesopelagic distribution
Notacanthidae Notacanthus bonapartei 0.419 Depth range*
Paralepididae Arctozenus risso Lestidiops jayarki Paralepis speciose Sudis hyalina
0.192
Depth range
Phosichthyidae Ichthyococcus ovatus Vinciguerria attenata Vincigueria poweriae
0.394
Depth range
Regalicidae Regalecus glesne 0.028 Mesopelagic distribution
Sternoptychidae Argyropelecus hemigymnus 2.962 Depth range
Stomiidae Bathophilus nigerrimus Borostomias antarcticus Chauliodus sloani Stomias boa boa
7.605 Mesopelagic distribution
*Macrouridae and Notacanthidae species are far from being mesopelagic species. However considering the biology of the fish, it has been included in the study.
x
Annex III: Distribution of variables from the samples
Distribution of Log-transformed Biomass
logbiomass
log(B
iom
ass)
-20 -15 -10 -5 0
0100
Distribution of Biomass
data2$x
Bio
mass
0 1 2 3 4 5
0300
Distribution of Salinity
Sal
Salin
ity
37.5 38.0 38.5 39.0
040
80
Distribution of Oxygen
Oxy
Oxygen
205 210 215 220 225 230 2350
40
Distribution of Bathymetry
bathy
Bath
ym
etr
y
-4000 -3000 -2000 -1000 0
030
60
Distribution of Temperature
Temp
Tem
pera
ture
15 16 17 18 19 20
060
Distribution of Primary production
PPR
Prim
ary
Pro
ductio
n
10 15 20
040
80
xi
Annex IV : Scatterplots of environmental variables and the biomass of samples
Oxy
45
67
837
.538
.038
.539
.0-1
5-1
0-5
0
205220235
45678
log.
.bat
hy.
PP
R
101520
37.538.5
Sal
Tem
p
151719
205
210
215
220
225
230
235
-15-50
1015
2015
1617
1819
20
logb
iom
ass
xii
Annex V: 1) Analyses of GAM residuals and uncertainty of 2) GAM and 3) RF
estimates
-10 -5 0 5 10
-10
-50
5
theoretical quantiles
devia
nce r
esid
uals
-9 -8 -7 -6 -5 -4 -3 -2
-10
-50
5
Resids vs. linear pred.
linear predictor
resid
uals
Histogram of residuals
Residuals
Fre
quency
-10 -5 0 5
050
150
250
-9 -8 -7 -6 -5 -4 -3 -2
-15
-10
-50
Response vs. Fitted Values
Fitted Values
Response
a)
b)
c)
xiii
Annex VI : Scientific literatures used for the extracting of the biomass.
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A. Bernal Bajo, 2014, Feeding ecology and community structure of mesopelagics fishes in the western Mediterranean, Doctora de Ciències del Mar, Universitat Politècnica de Catalunya, Barcelona
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J.E. Cartes, E. Fanelli, V. Papio I, L. Zucca, 2010, Distribution and diversity of open-ocean, near-bottom macroplankton in the western Mediterranean : Analysis at different spatio-temporal scales , Deep-sea Research I, Vol.57 (2010), 1485-1498
B. Cihangir, E. M. Tirasin, A. Unlüöglu, H.A. Benli, K. Can Bizsel, 2002, New records of Three deep-sea fishes : Diaphus rafinesquei (Myctophidae), Lobianchia gemellarii (Myctophidae), and Notolepis rissoi (Paralepididae) from the Aegan Sea (Turkish Coast) , Journal of Ichthyology, Vol.43(6) (2003), 486-489.
C. Dalyan, L. Eryilmaz, 2008, A new deepwater fish, Chauliodus sloani Bloch & Scneider, 1801 (Osteichthyes : Stomiidae), from the Turkish waters of Levant Sea (Eastern Mediterranean), Black Sea/Mediterranean Environment, Vol. 14 (2008), 33-37.
M. C. Deval, O. Güven, I. Saygu, T. Kabapçioglu, 2013, New records and uncommon occurreces of deep-water fishes in the Turkish Mediterranean Sea (Osteichthyes), Zoology in the Middle East, Vol. 59(4), 308-313
G. D'Onghia, F. Mastrototaro, A. Matarrese, C.-Y. Politou, Ch. Mytilineou, 2003, Biodiversity of the upper slope Demersal community in the Eastern Mediterranean : Preliminary Comparison in between two areas with and without trawl fishing, Journal Northwest Atlantic Fisheries Science, Vol. 31, 263-273.
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C. Garcia-Ruiz, D. Lloris, J.L. Rueda, M. C. Garcia-Martinez, L. Gil de Sola, 2015, Spatial distribution of ichthyofauna in the nothern Alboran Sea (Western Mediterranean), Journal of Natural History, Vol.49, No.19-20, 1191-1224
R.H. Goodyear, B.J. Zahuranec, W.L. Pugh, R.H.Jr Gibbs,1972, Mediterranean biological studies Final Report, Vol I, Washington, D.C. (USA) : Smithsonian Insitution
A. Kallianiotis, K. Sophronidis, P. Vidoris, A. Tselepides, 2000, Demersal fish and megafaunal assemblages on the Cretan continental shelf and slope (LE Mediterranean): seasonal variation in species density, biomass and diversity, Progress in Oceanography, Vol.46 (2000), 429-455.
M. Kaya, M. Bilecenoglu, 1999, New records of deep-sea fish in Turkish seas and the Eastern Mediterranean, Journal of Ichthyology, Vol.40(7) (2000), 543-547.
xiv
P. Laval, J.-C. Braconnot, C. Carré, J. Goy, P. Morand, C.E. Mills, 1989, Small-scale distribution of macroplankton and micronekton in the Ligurian Sea (Mediterranean Sea) as observed from the manned submersible Cyanam Plankton Research, Vol.11(4) (1989), 665-685
A. Lourie, 1969, Occurrence of twol Lanternfishes (Myctophidae) in the open sea off Mount Carmel (Israel), Israel Journal of Zoology, Vol.18, No.4, 379-380
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C. Stefanecu, J.E. Cartes, 1992, Benthopelagic habits of adult specimens of Lampanyctus crocodilus (Risso, 1810) (Osteichthyes, Myctophidae) in Western deep slope, Scientia Marina, Vol.56 (1) (1992), 69-74.
C.-Y. Politou, S. Kavadas, Ch. Mytilineou, A. Thrusiand, R. Carlucci, G. Lembo, 2003, Fisheries Resources in the Deep Waters of the Eastern Mediterranean (Greek Ionian Sea), Journal Northwest Atlantic Fisheries Sciences, Vol.31 (2003), 35-46
C.Papaconstantinou, K. Anastasopoulou, E. Caragitsou, 1997, Comments on the Mesopelagic fauna of the North Aegean Sea, Cybium, Vol.21(3) (1997), 281-288
C.-Y. Politou, C. Mytilineou, G. D'Onghia, J. Dokos, 2008, Demersal faunal assemblages in the deep waters of the easter Ionian Sea, Journal Of Natural History, Vol.43(5-8) (2008), 661-672.
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xv
Diplôme :Diplôme d’Ingénieur de l’institut Supérieur des Sciences agronomiques Agroalimentaires, Horticoles et du Paysage
Spécialité : Halieutique
Spécialisation / option : Ressources et écosystèmes aquatiques
Enseignant référent : RIVOT Etienne
Auteur(s) : CLAVEL-HENRY Morane
Date de naissance* : 16.04.1992
Organisme d'accueil : Institute for the Oceans and Fisheries (IOF)
Adresse : 2202 Main Mall, Vancouver V6T 1Z4
Canada
Maître de stage : Villy Christensen
Nb pages : 34 Annexe(s) : 6
Année de soutenance : 2015
Titre français : Estimation de la biomasse des poissons mésopélagiques à partir de données environnementales en mer Méditerranée.
Titre anglais : Estimates of the mesopelagic fish biomass according to environmental data in Mediterranean Sea
Résumé (1600 caractères maximum) : La biomasse des poissons mésopélagiques est un paramètre utile dans les modèles écosystémiques et dans le cycle du Carbone. En mer Méditerranée, la zone mésopélagique est soumise au changement climatique et à une modification de la faune marine. L’influence des paramètres environnementaux sur la biomasse des mésopélagiques (salinité, température, production primaire, oxygène et bathymétrie issus de modèles biogéochimiques) est étudiée au travers des modèles prédictifs du modèle additif généralisé (GAM) et Random Forest (RF) entre 1980 et 2011. L’analyse statistique s’est portée sur 905 biomasses extraites de littérature scientifique en Méditerranée. La plus influente variable, la salinité, impacte grandement la biomasse avec GAM tandis que RF est aussi sensible à la production primaire. Les deux méthodes indiquent une biomasse importante dans le bassin occidental et la biomasse totale s’avère faible : 20 000 à 35 000 t avec RF qui estime 35% en plus par rapport à GAM. La tendance de la biomasse en 31 ans varie sans nette augmentation ou diminution pour GAM. Pour RF, l’augmentation de la biomasse se fait graduellement jusqu’à 36 000 t en 2000 puis diminue. Bien que RF soit statistiquement une méthode satisfaisante pour l’estimation de la biomasse, elle nécessite des précautions quant à la fiabilité des résultats. RF et GAM donnant des résultats différents, conserver les deux méthodes et poursuivre cette étude sur l’ensemble des océans permettraient de conclure lequel des deux outils donne un modèle représentant le mieux la distribution écologique des mésopélagiques.
Abstract (1600 caractères maximum) :
Mesopelagic fish biomass is a useful parameter for ecosystem modelling and the understanding of the Carbon cycle. In Mediterranean Sea, the mesopelagic layer is affected by the climate change and a change in marine fauna. The impact of environmental parameter on mesopelagic fish biomass (salinity, temperature, primary production, oxygen and bathymetry from biogeochemical models) is modelled with a generalized additive model (GAM) and Random Forest (RF) between 1980 and 2011. The statistical analyze was done on 905 samples extracted from the scientific literature in Mediterranean Sea. The most influent variable – salinity – has a high impact on biomass with GAM whereas RF is also sensitive to the primary production. Both methods gave an important biomass in the Western basin. The whole biomass varies between 20,000 t and 35,000 t with RF estimating 35 % more than GAM. There is not a clear trend in the GAM estimates while RF estimates gradually rose up from 1980 to 2000 and decreased then. Although RF is a method statistically good; it needs to be carefully considered towards the reliability of the results. Giving the different results between GAM and RF, those methods will be kept for estimating on the global oceans in order to determinate which of them is the best adequate for the ecological distribution of mesopelagic fish biomass.
Mots-clés : poisson mésopélagique, Méditerranée, Random Forest, Modèle additif généralisé, estimation de biomasse,
Key Words: Mesopelagic fish, Mediterranean, Random Forest, Generalized Additive Model, Biomass estimate
* Elément qui permet d’enregistrer les notices auteurs dans le catalogue des bibliothèques universitaires
Document à déposer sur moodle en format .txt
![Welcome [halieutique.agrocampus-ouest.fr]halieutique.agrocampus-ouest.fr/pdf/3775.pdf · Welcome to the first edition of the GIFS newsletter. GIFS is an exciting ... Dr Tim Acott,](https://img.pdfslide.net/doc/110x75/5b9973bb09d3f29c338c55a5/welcome-welcome-to-the-first-edition-of-the-gifs-newsletter-gifs-is-an.jpg)
