Identifying the spatial pattern and the drivers of the decline in the eastern English Channel chlorophyll-a surface concentration over the last two decades - Antoine Huguet; Laurent Barillé; Dominique Soudant; Pierre Petitgas; Francis Gohin; Alain Lefebvre (Marine Pollution Bulletin, 2024)

The use of TNF-α antagonists in tuberculosis to control severe paradoxical reaction or immune reconstitution inflammatory syndrome: a case series and literature review - Lucas Armange; Adèle Lacroix; Paul Petitgas; Cédric Arvieux; Caroline Piau-Couapel; Patrice Poubeau; Matthieu Revest; Pierre Tattevin (European Journal of Clinical Microbiology & Infectious Diseases, 2023)

Hydrology and small pelagic fish drive the spatio–temporal dynamics of springtime zooplankton assemblages over the Bay of Biscay continental shelf - Nina Grandremy; Jean-Baptiste Romagnan; Christine Dupuy; Mathieu Doray; Martin Huret; Pierre Petitgas (Progress in Oceanography, 2023)

Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future - Mary C. Fabrizio; Mark J. Henderson; Kenneth Rose; Pierre Petitgas (Frontiers in Marine Science, 2022)

Analysing Temporal Variability in Spatial Distributions Using Min–Max Autocorrelation Factors: Sardine Eggs in the Bay of Biscay - Pierre Petitgas; Didier Renard; Nicolas Desassis; Martin Huret; Jean-Baptiste Romagnan; Mathieu Doray; Mathieu Woillez; Jacques Rivoirard (Mathematical Geosciences, 2020)

Indicator-Based Geostatistical Models For Mapping Fish Survey Data - Pierre Petitgas; Mathieu Woillez; Mathieu Doray; Jacques Rivoirard (Mathematical Geosciences, 2018)

A Geostatistical Definition of Hotspots for Fish Spatial Distributions - Pierre Petitgas; Mathieu Woillez; Mathieu Doray; Jacques Rivoirard (Mathematical Geosciences, 2016)

Impacts of climate change on the complex life cycles of fish - Pierre Petitgas; Adriaan D. Rijnsdorp; Mark Dickey‐Collas; Georg H. Engelhard; Myron A. Peck; John K. Pinnegar; Ken Drinkwater; Martin Huret; Richard D. M. Nash (Fisheries Oceanography, 2013)
To anticipate the response of fish populations to climate change, we developed a framework that integrates requirements in all life stages to assess impacts across the entire life cycle. The framework was applied on plaice ( P leuronectes platessa ) and Atlantic herring ( C lupea harengus ) in the N orth S ea, A tlantic cod ( G adus morhua ) in the N orwegian/ B arents S eas and E uropean anchovy ( E ngraulis encrasicolus ) in the B ay of B iscay. In each case study, we reviewed habitats required by each life stage, habitat availability, and connectivity between habitats. We then explored how these could be altered by climate change. We documented environmental processes impacting habitat availability and connectivity, providing an integrated view at the population level and in a spatial context of potential climate impacts. A key result was that climate‐driven changes in larval dispersion seem to be the major unknown. Our summary suggested that species with specific habitat requirements for spawning (herring) or nursery grounds (plaice) display bottlenecks in their life cycle. Among the species examined, anchovy could cope best with environmental variability. Plaice was considered to be least resilient to climate‐driven changes due to its strict connectivity between spawning and nursery grounds. For plaice in the N orth S ea, habitat availability was expected to reduce with climate change. For N orth S ea herring, N orwegian cod and B iscay anchovy, climate‐driven changes were expected to have contrasting impacts depending on the life stage. Our review highlights the need to integrate physiological and behavioural processes across the life cycle to project the response of specific populations to climate change.

Understanding what controls the spatial distribution of fish populations using a multi‐model approach - BENJAMIN PLANQUE; CHRISTOPHE LOOTS; PIERRE PETITGAS; ULF LINDSTRøM; SANDRINE VAZ (Fisheries Oceanography, 2011)
Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology. The spatial distribution of fish populations observed in the wild results from the complex interactions of multiple controls both external or internal to the fish populations. Whilst species distribution models (SDMs) have been mostly concerned with static description of species distribution as a function of environmental constraints, models of animal movements (MAMs) have focussed on the dynamic nature of spatial distribution of groups of individuals under a number of constraints external and internal to the population. Besides SDMs and MAMs, modelling the spatial distribution of fish populations can be achieved by models that are fundamentally static (like SDMs) but can also incorporate many hypotheses on the control of fish spatial distribution (like MAMs). The hypotheses underlying these models need to make sense at the population level ‐ rather than at the individual or species level –we term these ‘population distribution models’ (PDMs). PDMs are statistical models that rely on several hypotheses, which include: (i) control through geographical attachment, (ii) environmental conditions, (iii) density‐dependent habitat selection, (iv) spatial dependency, (v) population demographic structure, (vi) species interactions and (vii) population memory. We review the basis behind each of these conceptual models and we examine corresponding numerical applications. We argue that the conceptual models are complementary rather than competing, that existing numerical applications are still rarely compared and combined, and that PDMs can offer a statistical framework to achieve this. We recommend that the numerical models associated with different hypotheses be constructed within such a common general framework. This will permit evaluation, comparison and combination of the multiple hypotheses on fish spatial distribution. It will ultimately lead to a more comprehensive understanding of the factors controlling the spatial distribution of fish populations and to more accurate predictions in which model uncertainty is accounted for.

Biophysical modelling of larval drift, growth and survival for the prediction of anchovy ( Engraulis encrasicolus ) recruitment in the Bay of Biscay (NE Atlantic) - GWENHAEL ALLAIN; PIERRE PETITGAS; PASCAL LAZURE; PATRICK GRELLIER (Fisheries Oceanography, 2007)
Fish recruitment is the result of the integration of small‐scale processes affecting larval survival over a season and large oceanic areas. A hydrodynamic model was used to explore and model these physical–biological interaction mechanisms and then to perform the integration from individual to population scales in order to provide recruitment predictions for fisheries management. This method was applied to the case of anchovy ( Engraulis encrasicolus ) in the Bay of Biscay (NE Atlantic). The main data available to investigate survival mechanisms were past growth (otolith) records of larvae and juveniles sampled at sea. The drift history of these individuals was reconstructed by a backtracking procedure using hydrodynamic simulations. The relationships between (real) growth variation and variations in physical parameters (estimated by hydrodynamic simulations) were explored along the individual trajectories obtained. These relationships were then used to build and adjust individual‐based growth and survival models. Thousands of virtual buoys were released in the hydrodynamic model in order to reproduce the space–time spawning dynamics. Along the buoy trajectories (representative of sub‐cohorts), the biophysical model was run to simulate growth and survival as a function of the environment encountered. The survival rate after 3 months of drift was estimated for each sub‐cohort. The sum of all these survival rates over the season constituted an annual recruitment index. This index was validated over a series of recruitment estimations. The modelling choices, model results and the potential use of the recruitment index for fisheries management are discussed.

The influence of environment and spawning distribution on the survival of anchovy ( Engraulis encrasicolus ) larvae in the Bay of Biscay (NE Atlantic) investigated by biophysical simulations - GWENHAEL ALLAIN; PIERRE PETITGAS; PASCAL LAZURE (Fisheries Oceanography, 2007)
A growth and survival model of the early life stages was run along virtual drift trajectories tracked in a hydrodynamic model to simulate the annual recruitment process of anchovy ( Engraulis encrasicolus ) in the Bay of Biscay (NE Atlantic). These biophysical simulations concerning three different years were analysed in order to investigate the influence of environment and spawning dynamics on the survival of larvae and juveniles. The location of space–time survival windows suggested major environmental mechanisms involved in simulated recruitment variability at the different scales – retention of larvae and juveniles in favourable habitats over the shelf margins and turbulence effects. These small‐scale and meso‐scale mechanisms were related to the variations in wind direction and intensity during spring and summer. Survival was also variable according to the origin of the drift trajectories, that is spawning distribution in space and time. The observed spawning distribution (according to field surveys) was compared with the spawning distribution that would maximize survival (according to the biophysical model) on a seasonal scale, which revealed factors not considered in the biophysical model (e.g. spawning behaviour of the different age classes). The variation of simulated survival according to spawning distribution was examined on a multi‐annual scale and showed a coherent pattern with past and present stock structures. The interaction processes between the population (influence on spawning) and its environment (influence on survival) and its implications on recruitment and stock dynamics are discussed.

Anchovy (Engraulis encrasicolus) egg density measurements in the Bay of Biscay: evidence for the spatial variation in egg density with sea surface salinity - Anne Goarant; Pierre Petitgas; Paul Bourriau (Marine Biology, 2007)

Historical fluctuations in spawning location of anchovy ( Engraulis encrasicolus ) and sardine ( Sardina pilchardus ) in the Bay of Biscay during 1967–73 and 2000–2004 - EDWIGE BELLIER; BENJAMIN PLANQUE; PIERRE PETITGAS (Fisheries Oceanography, 2007)
The spatial extent of small pelagic fish spawning habitat is influenced by environmental factors and by the state of the adult population. In return, the configuration of spawning habitat affects recruitment and therefore the future structure of the adult population. Interannual changes in spatial patterns of spawning reflect variations in adult population structures and their environment. The present study describes the historical changes in the spatial distribution of spawning of anchovy ( Engraulis encrasicolus ) and sardine ( Sardina pilchardus ) in the Bay of Biscay during two periods: 1967–72 and 2000–2004. Using data from egg surveys conducted in spring, the spatial distributions of anchovy and sardine eggs are characterized by means of geostatistics. For each survey, a map of probability of egg presence is constructed. The maps are then compared to define (1) recurrent spawning areas, (2) occasional spawning areas and (3) unfavourable spawning areas during each period. Sardine spawning habitat is generally fragmented and appears spatially limited by the presence of cold bottom water. It is confined to coastal or shelf break refuge areas in years of restricted spawning extent. For anchovy, recurrent spawning sites are found in Gironde and Adour estuaries whilst spawning can extend further offshore in years of more intense spawning. For both species, the mean pattern of spawning has changed between 1967–72 and 2000–2004. Noticeably, the spatial distribution of anchovy eggs in spring has expanded northward. This trend possibly results from changes in environmental conditions during the last four decades.

One‐dimensional biophysical modelling of fish egg vertical distributions in shelf seas - PIERRE PETITGAS; STÉPHANIE MAGRI; PASCAL LAZURE (Fisheries Oceanography, 2006)
Modelling the vertical distribution of fish eggs is important when assessing fish stocks with egg production methods and for monitoring the reproductive potential of fish populations. Fish eggs are passive particles and their vertical distribution is determined by a few parameters such as egg density, egg diameter, wind‐ and tide‐induced turbulence, and vertical hydrographic structure. A one‐dimensional vertical biophysical, numerical model was developed which was adapted to the hydrography of shelf seas under the influence of tidal currents, wind‐induced circulation, and river discharges. The biological part of the model parameterized the ascent velocity of the egg as a function of egg properties (diameter, density) and water properties (density, viscosity, turbulence). The model contains a turbulence closure which makes the model dynamic. The model parameters were surface wind, tidal currents, T‐S profile, and egg diameter and density, which were kept constant in time. The model has the capacity to generate sub‐surface egg maxima in different hydrographic conditions, e.g. in areas under the influence of river plumes, and can also homogenize the egg distribution under wind and tide forcing. Sensitivity tests were carried out to study the response of the model to variations in the model parameters for a variety of hydrographic conditions. The modelled egg vertical distributions were validated by comparison of the model results with egg distributions sampled in the field. The analysis highlighted variability in fish egg density of anchovy, sardine, and sprat across years and areas, with a potential link between egg density and surface sea water density. The validated model is a tool for the analysis of shelf seas fish egg vertical distributions.

The selection process from larval to juvenile stages of anchovy ( Engraulis encrasicolus ) in the Bay of Biscay investigated by Lagrangian simulations and comparative otolith growth - Gwenhael Allain; Pierre Petitgas; Patrick Grellier; Pascal Lazure (Fisheries Oceanography, 2003)
The relation between growth and survival was investigated using the otolith growth data collected during repeated larval surveys (May–July) and a juvenile survey (September) undertaken in 1999 on anchovy spawning and nursery grounds in the Bay of Biscay (NE Atlantic). The paper describes the methodology for reading the larval and juvenile otoliths, for reconstructing the correspondence in space and time between juveniles and larvae using Lagrangian simulations, and for comparing the otolith growth rates among the reconstructed sub‐cohorts. Virtual buoys were released weekly on the grid of a three‐dimensional hydrodynamic model and their trajectories were tracked. The origin of an individual was determined by selecting the trajectory beginning on its hatching week and ending at the minimum distance of its sampling location on the sampling date. Larvae and juveniles with the same spatio‐temporal origin were selected and supposed to belong to the same sub‐cohorts. The surviving juveniles showed faster growth rates during their larval period than the pool of larvae they were estimated to originate from, which supports the idea of growth‐selective survival. Alternative interpretations (transport and gear selectivity) are discussed. Variations in otolith growth pattern also suggest a higher juvenile growth over the shelf break than in oceanic waters.

Geostatistics in fisheries survey design and stock assessment: models, variances and applications - Pierre Petitgas (Fish and Fisheries, 2001)
Over the past 10 years, fisheries scientists gradually adopted geostatistical tools when analysing fish stock survey data for estimating population abundance. First, the relation between model‐based variance estimates and covariance structure enabled estimation of survey precision for non‐random survey designs. The possibility of using spatial covariance for optimising sampling strategy has been a second motive for using geostatistics. Kriging also offers the advantage of weighting data values, which is useful when sample points are clustered. This paper discusses, with fisheries applications, the different geostatistical models that characterise spatial variation, and their variance formulae for many different survey designs. Some anticipated developments of geostatistics related to multivariate structures, temporal variability and adaptive sampling are discussed.